Machine Learning Engineer Remote Jobs in Indiana (US)
This page tracks remote machine learning engineer openings that are location-eligible for Indiana.
This page tracks remote machine learning engineer openings that are location-eligible for Indiana.
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• Diagnose business problems before building solutions • Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration • Design, develop, and ship AI-powered solutions quickly • Improve organizational flow by building solutions that reduce bottlenecks • Integrate AI capabilities into existing systems and workflows • Partner closely with stakeholders across functions • Define and track success through business metrics and feedback loops
Role Description Our client, a growing educational and technology organization, is seeking an experienced AI & Machine Learning Instructor to teach and mentor students on artificial intelligence, machine learning engineering, intelligent systems development, and practical strategies for becoming successful AI & Machine Learning Engineers. This role is ideal for an experienced AI or Machine Learning professional who is passionate about teaching and sharing real-world industry knowledge with aspiring AI engineers, data professionals, and software developers. - Deliver engaging training sessions on artificial intelligence, machine learning, and intelligent systems development. - Teach students how to design, build, train, evaluate, and deploy machine learning models and AI-powered applications. - Guide students on supervised learning, unsupervised learning, deep learning, neural networks, and AI engineering workflows. - Share practical experiences, case studies, and real-world AI and machine learning project insights with students. - Teach students programming concepts using Python and relevant AI/ML frameworks and tools. - Train students on data preprocessing, model optimization, feature engineering, and AI deployment techniques. - Develop instructional materials, coding exercises, presentations, and hands-on AI projects. - Facilitate workshops, live coding demonstrations, and project-based learning sessions. - Mentor students on portfolio development, technical problem-solving, and AI career pathways. - Stay updated on emerging AI technologies, machine learning advancements, Generative AI, and industry best practices. Qualifications - Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Technology, or related field required. - Master’s degree or advanced certification in AI, Machine Learning, or Data Science is an advantage. - Minimum of 4–5 years of practical experience in artificial intelligence, machine learning engineering, data science, or related technology fields. - Strong understanding of machine learning algorithms, deep learning, AI engineering workflows, and data-driven systems. - Experience working with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, or similar technologies. - Proficiency in Python and familiarity with APIs, cloud AI tools, databases, and deployment technologies. - Excellent communication, presentation, and mentoring skills. - Ability to explain technical concepts clearly and engage students effectively. - Strong analytical, coding, and problem-solving abilities. - Must be legally authorized to work in the USA or Canada. Preferred Qualifications - Experience delivering technical training, workshops, or mentoring programs. - Familiarity with NLP, Generative AI, LLMs, computer vision, or AI automation tools. - Experience building and deploying AI-powered solutions in commercial or production environments. - Certifications in AI, machine learning, cloud technologies, or data science are an advantage. Requirements - Part time. Pay depends on experience.
QuinStreet offers a decentralized online marketplace that empowers consumers by matching them with brands that meet their needs. A leader among “research and compare” networks,
Role Description We are looking for a Senior AI Developer & Cloud Architect to design, build, and own the AI-powered compliance scraping engine and cloud infrastructure layer for an internal platform monitoring up to 70,000 credit card offer pages per month. This is a hands-on, sole-builder contractor role that sits at the intersection of cloud architecture, AI engineering, and large-scale web scraping, with a clear mandate: deliver a production-grade system that detects compliance violations across issuer offer pages with high accuracy and controlled token costs. You will do this by: - Architecting the AWS environment from the ground up. - Building a containerized worker fleet that integrates Playwright rendering with Claude-powered contextual analysis. - Defining clean API contracts with the internal team that owns the Laravel control panel. This is not a managed-PaaS or prototype role. You will be accountable for end-to-end delivery — architecture, build, documentation, and knowledge transfer — owning the full scraping and AI pipeline from URL intake through compliance findings, screenshot evidence, and results delivery back to the portal. Responsibilities - Design and configure the production AWS environment (ECS/Fargate, SQS, API Gateway, RDS PostgreSQL, S3, IAM, CloudWatch) using infrastructure as code (Terraform or CDK). - Build a stateless, containerized worker fleet that integrates Playwright-based page rendering, structured rule evaluation, and Claude API analysis. - Implement token optimization strategies across the LLM pipeline — prompt engineering, context pruning, caching, model selection, and batching — with measurable cost outcomes. - Define and document API contracts, job payload schemas, and database write patterns with the internal Laravel portal team to enable parallel development. - Build third-party API ingestion and field-level diff-detection logic that automatically adjusts monitoring rules when product data changes. - Handle modern web rendering challenges at scale: JavaScript-heavy SPAs, interstitials, cookie consent overlays, dynamic content, viewport switching, and full-page screenshot capture. - Evaluate when LLM analysis is the correct tool versus a classifier or rules-based approach, and design the two-stage rule-engine-plus-AI pipeline accordingly. - Build and maintain a unit test suite covering all modules and APIs to ensure uptime and proper functionality. - Document every architecture decision, configuration, API contract, and operational procedure continuously — not as a final-week deliverable. - Deliver a complete runbook and knowledge transfer to the internal team at engagement close. - Operate independently end-to-end while coordinating closely with the internal portal team and reporting directly to the Senior Director, surfacing risks and trade-offs early. Requirements - Production backend Python experience, including async patterns, type hints, packaging, and testing. - Direct production experience designing and configuring AWS ECS/Fargate, SQS, API Gateway, RDS (PostgreSQL), S3, IAM, and CloudWatch, with infrastructure as code (Terraform or CDK). - Real shipped systems calling the Anthropic Claude API in production, with demonstrated experience in prompt design, structured output, error handling, and cost trade-offs. - Demonstrated track record reducing token spend on production LLM workloads, with specific before/after results you can walk through. - Working knowledge of other LLM providers sufficient to recommend cheaper or better alternatives for specific tasks. - Production Playwright experience at scale, including headless Chromium failure modes, network idle detection, dynamic content handling, viewport switching, and screenshot strategy. Selenium or Puppeteer experience does not substitute. - Machine learning fundamentals sufficient to evaluate when LLM analysis is the right tool versus a classifier or rules-based approach, and to reason about evaluation and false-positive rates. - Docker and containerization experience, including image optimization, ECR, and stateless worker design. - Ability to operate fully independently — no engineering team underneath you — while documenting continuously and coordinating cleanly with an internal team. Nice to Have - Experience with API ingestion and field-level diff-detection systems. - Laravel or PHP familiarity, enough to coordinate cleanly on API contracts with the portal team. - SOC 2 Type II compliance experience. - Salesforce API integration experience. - Regulated-industry experience (financial services, healthcare, or insurance). Benefits - The expected hourly range for this position is $80/hr - 100/hr. This hourly range is an estimate, and the actual hourly rate may vary based on the Company’s compensation practices. - The hourly rate may be adjusted based on applicant's geographic location. - This position is eligible to participate in the Company’s standard employee benefits programs, which currently include health care benefits. Company Description QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law. Please see QuinStreet’s Employee Privacy Notice here.
• Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support. • Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business. • Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling. • Improve the ML development experience by building better operational patterns and advancing production-ready ML practices. • Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production. • Collaborate with technical and business partners to solve high-value problems and improve the reliability and scalability of ML systems. • Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations.
• Design, build, and maintain AI agents, integrations, and automations that reduce manual work, eliminate bottlenecks, and improve productivity across every department at Chipply. • Inspect Chipply's internal SaaS platforms (HubSpot, Microsoft 365, Confluence, Slack, and others) to understand what APIs, webhooks, and MCP connectors they expose, and use those surfaces to build the integrations each team needs. • Partner with leadership and department heads to identify and prioritize high-impact opportunities by mapping existing workflows and pinpointing where engineering effort — AI-powered or deterministic — will deliver measurable improvement. • Write production code. Build multi-tool and multi-agent systems that connect Chipply's internal platforms into seamless end-to-end processes. Make sound judgment calls on when AI is the right tool and when a deterministic solution is a better fit. • Build evals and monitoring for AI systems you ship, so output quality is measurable rather than assumed. • Serve as Chipply's internal AI champion by leading education, training, and adoption efforts, and by acting as the go-to engineering resource for AI questions, best practices, and emerging use cases. • Establish and maintain responsible AI governance, including guidelines for data handling, privacy, model and tool selection, output quality monitoring, and ethical use. • Continuously evaluate emerging AI tools, agent frameworks, model providers, and orchestration libraries; pilot promising technologies and recommend adoption decisions based on impact, cost, and fit. • Measure and report on the ROI of the systems you ship, tracking time saved, error reduction, cost impact, and quality improvements. • Develop and maintain documentation for the systems, integrations, and AI workflows you build so institutional knowledge scales with the company. • Serve as a liaison across Engineering, Finance, Sales, Marketing, Customer Support, and Product to align on technology needs and ship workflow improvements end to end. • Monitor security and compliance across the integrations and AI systems you build, collaborating with engineering and leadership to ensure data protection best practices are followed. • Continuously redefine the scope, priorities, and deliverables of the role as Chipply's needs and the broader AI ecosystem evolve.
Role Description Tealium is seeking a Senior Engineer, AI Developer Tools, to contribute to the vision, design, and rollout of our next-generation AI-driven developer tools. This is a high-impact role at the core of a strategic initiative to modernize Tealium’s engineering ecosystem, enabling teams with intelligent, automated, and scalable tooling. In this role, you will directly empower Tealium’s development teams to innovate faster, enhance code quality, and dramatically increase engineering productivity through AI. This is a unique opportunity to lead a critical strategic initiative, influence technical direction, and help teams adopt AI-forward development practices in a measurable, practical way across the engineering organization. As the driving force behind AI Developer Tools, your work will accelerate both engineering and business velocity by: - Accelerating Product Modernization, Engineering Velocity and Quality: Designing, building, and owning internal AI-driven developer platforms and tools (such as code review, test generation, migration automation, and CI/CD copilots) to achieve measurable improvements in engineering productivity and code quality. - Defining evaluation frameworks, orchestration, and governance (guardrails) for integrating AI safely into the developer workflow to tackle technical debt and accelerate modernization initiatives (e.g., AWS SDK migrations, Java 21 upgrades). - Leading a critical strategic initiative, influencing technical direction, and helping teams adopt AI-forward development practices. Qualifications - 5+ years of software engineering experience, with at least 3 years leading large-scale internal tooling, platform engineering, or modernization efforts. - Proven success designing and delivering AI-powered developer tools—beyond experimentation—with real-world measurable impact on team velocity and quality. - Deep understanding of modern software development practices, languages, and environments (e.g., Java, TypeScript, CI/CD, cloud infrastructure). - Experience using strategic thinking to drive solutions for complex, undefined problems within enterprise-scale engineering organizations. - Strong architecture and system design skills, with the ability to make build-vs-buy decisions and scale tooling across organizations. - Demonstrated ability to influence with authority, collaborate across disciplines, and lead by example in a senior or principal engineering capacity. - Exceptional cross-functional communication skills, including the ability to clearly present complex technical concepts and translate engineering efforts into business value. - A passion for improving the developer experience and a practical mindset toward applying AI to solve real engineering problems. - Experience evaluating AI tools and models across critical dimensions, including quality, cost, latency, security, and workflow fit. - Familiarity with tools like Claude Code, GitHub Copilot, AWS Q, or other generative AI platforms. - Experience applying AI/automation to software development workflows with measurable improvements in speed, quality, or reliability. Requirements - Drive AI-Powered Refactoring of Monolithic Platform Components. - Design and Implement AI-Enhanced Code Review and Test Automation. - Lead Measurement and Adoption of AI Tools. - Evaluate and integrate best-in-class AI technologies. - Establish and scale repeatable AI-assisted development workflows. - Serve as a technical authority and thought leader across the engineering organization. - Drive large-scale modernization initiatives. - Stay current with emerging AI and developer productivity trends. - Articulate technical vision and strategic decisions to senior stakeholders. - Drive multi-week implementations of AI-powered developer tools. Benefits - Employees are eligible to receive an annual bonus and stock options. - Employees and their families are eligible for medical, dental, vision, life, and disability insurance. - Employees have the option to enroll in our 401k plan and are eligible for contributions for company matching. - Employees are eligible for flexible paid time-off and extended paid parental leave. - We offer 11 paid holidays annually with an additional Healium Be-Well break for most employees. - We offer 15 hours of paid work time for volunteer activities and programs. - Sick leave accrual varies by employee classification and location. Wage Transparency The U.S. pay range for this full-time position is $145,000 - $185,000 + Variable + Equity Options. Base pay offered may vary depending on job-related knowledge, skills, and experience. Why You Want to Work Here - Tealium WOWs (Ways of Work), our award-winning culture. - Mosaic, our commitment to diversity, equity, and inclusion. - Tealium Cares, promoting caring in our communities. - Tealium Connects (remote-first working). - Tealium Ownership, share in the success of Tealium. - Tealium Time, paid time-off policy for flexibility. - Healium, health and wellness programs. - Tealium LIFT (Learning is Facilitated at Tealium), offering professional development opportunities. - Health and Related Benefits Programs, offering market competitive benefits.
Sheetz is committed to the full inclusion of all qualified individuals. Sheetz is committed to considering all applicants regardless of disability who can perform all essential job duties with or without accommodations.
Role Description A Senior Machine Learning Ops Engineer at Sheetz ensures that AI models move seamlessly from “working on a laptop” to running reliably across our stores, applications, and systems at scale. This role powers capabilities like smarter inventory management, enhanced customer experiences, and faster decision-making that keeps pace with the way Sheetz operates. The MLOps Engineer designs, builds, and maintains the pipelines, deployment processes, and monitoring systems that allow models to run continuously and perform consistently. Just as Sheetz kitchens operate around the clock to serve customers, this role keeps our AI systems running 24/7, using data as the ingredients and algorithms as the recipes that drive our technology. This role qualifies for a remote work arrangement within our 7 state footprint (PA, OH, MI, WV, VA, MD, NC). Responsibilities - Lead the end-to-end development and optimization of ML pipelines, including training, validation, deployment, monitoring, and retraining workflows at scale. - Guide the use of and implement infrastructure for tools such as ML flow, TensorFlow, PyTorch, Docker, and Kubernetes to support scalable production workflows for model deployment and lifecycle management. - Design and monitor tools for performance monitoring, drift detection, and automated alerting. - Develop CI/CD pipelines to enable safe, rapid model iteration, deployment, and retraining across environments. - Write, review, and maintain high-quality, production ready code, ensuring robust, reproducible, and secure ML systems. - Apply advanced software engineering and ML Ops best practices to operationalize machine learning solutions efficiently and reliably. - Collaborate with cross-functional teams to align ML solutions with business needs and system requirements and guide integration efforts to embed ML into production applications. - Maintain thorough documentation, version control, metadata tracking, and lineage to support reproducibility and compliance of ML models. - Recommend and implement improvements to ML infrastructure, frameworks, and operational standards, elevating the organization’s ML maturity and capabilities. - Mentor and coach junior engineers, providing guidance on technical challenges, workflow design, and career development. Qualifications - Bachelor’s degree in Computer Science, Management Information Systems, Computer Engineering, or related discipline is required. - Minimum 5 years hands-on experience in designing, developing, and operationalizing machine learning solutions, with a strong focus on ML Ops practices and infrastructure is required. - Previous experience working with large databases – both structured and unstructured – to build data pipelines and self-service dashboards for business users required. - Previous experience in managing machine learning pipelines, lifecycle management, and deployment at scale—including training, validation, serving, and monitoring required. - Previous experience with CI/CD pipelines for ML workflows and containerization tools such as Docker and Kubernetes preferred. - Previous experience with secure and scalable cloud environments (e.g., AWS, GCP, Azure) and infrastructure-as-code and platform-as-a-service (PaaS) offerings preferred. - Cloud Platforms (AWS, GCP, Azure) preferred. - MLOps tools and frameworks (e.g., ML Flow, Kubeflow, TFX) preferred. - DevOps certifications (e.g. Docker, Kubernetes, Terraform, CI/CD Tools) preferred. Company Description
We are a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health.
• Design, implement, and evaluate deep learning models across biomedical data modalities • Develop multimodal AI architectures integrating H&E whole-slide imaging data with molecular and clinical data sources • Build scalable, production-quality ML workflows and pipelines using cloud infrastructure (AWS) • Apply modern ML techniques including CNNs, vision transformers (ViTs), sequence transformers, representation learning, and foundation model fine-tuning • Collaborate with technical and clinical teams to translate ML prototypes into validated tools • Analyze model outputs to generate reproducible biological and clinical insights • Document pipelines thoroughly and communicate data-driven findings to stakeholders
Role Description Reporting to the Director of AI Engineering, the Lead AI Engineer is responsible for building, managing, and scaling Gifthealth's AI engineering function. This position combines hands-on technical work with team leadership: architecting and implementing agentic AI systems while building, developing, and leading a team of AI Engineers. The Lead AI Engineer drives technical decisions across LLM orchestration, RAG pipelines, and production AI infrastructure, while establishing engineering practices, R&D functions, and growing team capabilities. We are seeking a Lead AI Engineer to partner with ML Engineers, Prompt Engineers, and Product teams, ensuring alignment with organizational goals, operational excellence, and compliance standards. Key Responsibilities - Designs and builds agentic AI systems including LLM orchestration, RAG pipelines, and multi-agent frameworks - Leads, mentors, and grows AI Engineers, conducting one-on-one meetings, performance reviews, and identifying career development opportunities - Works with the Talent Acquisition team to recruit and hire AI Engineers through defining role requirements, screening candidates, and leading technical interviews - Establishes engineering practices, coding standards, and technical processes for AI systems - Partners with ML Engineers, Prompt Engineers, and Product on cross-functional AI initiatives - Drives build/buy/integrate decisions; evaluates AI tooling and vendor solutions Qualifications - Bachelor’s degree in computer science, AI/ML, or a related field OR 2–4 years of equivalent practical experience (Required) - Master’s degree in computer science or AI/ML (Preferred) - 6+ years of software engineering experience with 3+ years in AI/ML systems; 2+ years of management or tech lead experience; history of building and shipping production AI systems (Required) - Experience in healthcare or regulated industries; history of building teams from scratch; FDA or clinical trial software experience (Preferred) - Knowledge of LLM architectures and orchestration patterns; RAG systems and vector databases; production AI/ML system design; software engineering best practices; and leadership and management principles (Required) - Knowledge of the healthcare domain and HIPAA compliance; FDA software validation; human-in-the-loop ML systems; graph databases and knowledge graphs (Preferred) - Python and AI/ML development skills (Required) - LLM API integration skills (Required) - System architecture and design skills (Required) - Technical hiring and interviewing skills (Required) - Team leadership and mentorship skills (Required) - GPU-accelerated processing skills (Preferred) - Distributed systems skills (Preferred) - Cloud platform (AWS, GCP) skills (Preferred) - ML Ops tooling skills (Preferred) - Ability to balance hands-on building with team leadership (Required) - Ability to hire and develop engineering talent (Required) - Ability to drive technical decisions across teams (Required) - Ability to communicate with technical and non-technical stakeholders (Required) - Ability to navigate ambiguity in early-stage product development (Preferred) - Ability to establish engineering culture and practices from scratch (Preferred) - Ability to influence without authority across functions (Preferred) Work Environment - Location: Remote - Schedule: 8:00 A.M. to 5:00 P.M. Monday through Friday with night and weekend hours on occasion as determined by the needs of the business. - Regular meetings with internal AI Engineering, Prompt Engineering, ML Engineering, Software Engineering, ML Ops Engineering, Data Science, HR, and product management teams. This role may also have meetings with AI/LLM, cloud, and ML Ops vendor and technology partner representatives. - Must be able to remain in a stationary position for extended periods while writing or reviewing documentation - Must be able to work on a computer for the entire shift - Must be able to attend virtual meetings with cross-functional teams Employment Classification - Status: Full-time - FLSA: Exempt Equal Employment Opportunity (EEO) Statement Gifthealth is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. All employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, transgender status, national origin, age, disability, veteran status, or any other legally protected status. We celebrate diversity and are committed to creating an inclusive environment for all employees. If you do not meet every requirement but still feel you would be a great fit for this role, we encourage you to apply! Disclaimer This job description is intended to describe the general nature and level of work being performed. It is not intended to be an exhaustive list of all responsibilities, duties, or skills required of personnel. Gifthealth reserves the right to modify job duties or descriptions at any time.
At Guild, we unlock opportunity for America’s workforce through education, skilling, and career mobility.
Role Description Guild is seeking a Senior MLOps Engineer . As a Senior ML Ops Engineer, you'll be pivotal in designing and implementing infrastructure and tooling that allows teams to efficiently develop, deploy, and iterate on machine learning models and AI agents. Your contributions will enable rapid innovation, consistent reliability, and effective scaling of Guild's AI capabilities. This is a role that will be pivotal in establishing Guild’s ML / AI platform. Qualifications - 5–7 years of experience in MLOps, DevOps, software engineering, or related fields. - Strong experience in building and maintaining scalable machine learning infrastructure and pipelines. - Expertise with cloud platforms (AWS, Azure, or GCP), particularly in managed AI/ML services. - Proficiency with containerization (Docker, Kubernetes) and orchestration tools. - Experience in MCP (model context protocol); any specific experience with Databricks MCP or AWS MCP is a plus. - Experience in model deployment frameworks and serving infrastructure (TensorFlow Serving, TorchServe, FastAPI, etc.). - Skilled in infrastructure-as-code tools like Terraform and familiarity with CI/CD automation (GitHub Actions, Jenkins). - Deep understanding of ML lifecycle management, monitoring, version control, and experiment tracking tools (e.g., MLflow, Kubeflow, Weights & Biases). - Strong coding skills, especially in Python, and familiarity with software engineering best practices. - Knowledge of monitoring, logging, and alerting systems for ML models in production. Requirements - Design, implement, and maintain platforms for seamless deployment, management, and monitoring of ML models and AI agents. - Develop and optimize CI/CD pipelines tailored specifically for AI and machine learning workflows. - Collaborate closely with data scientists, software engineers, and product teams to streamline ML model productionization. - Ensure infrastructure is scalable, secure, and adheres to best practices in reliability and observability. - Provide technical leadership in adopting best practices for model governance, versioning, testing, and validation. - Continuously improve platform performance, efficiency, and ease-of-use to accelerate development cycles. - Mentor team members on MLOps standards, practices, and emerging technologies. Benefits - Access to low-cost, high-quality health care options through Collective Health and Kaiser (due to coverage limitations, Kaiser is currently only available in CA & CO). - Access to a 401k to help save for the future. - Vacation policy to rest and recharge. - 8 days of fully-paid sick leave, to take the time to heal and or recover. - Family-friendly benefits, including 12 weeks of parental leave for non-birthing parents and 18-20 weeks for birthing parents; 2-week ramp-up period for when employees return from a leave of 6 weeks or more; as well as employer-paid short-term and long-term disability, employer-sponsored life insurance, fertility and caregiving benefits. - Well-rounded wellness benefits including free and low cost mental health resources and financial wellbeing support services. - Education benefits and tuition assistance to help your future development and growth.
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