This page tracks remote ai engineer openings that are location-eligible for Maine.
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An independent, tech-enabled payment integrity company.
• Design, develop, test, and ship production‑grade software across backend services and front‑end applications. • Build and operate cloud‑native solutions in AWS with a focus on reliability, scalability, and performance. • Develop and maintain backend services primarily in .NET/C#. • Contribute to Angular front‑end development, including component design and API integration. • Implement and support CQRS patterns, separating command and query responsibilities where appropriate. • Design and work with event‑sourced systems, including events, projections/read models, and system evolution over time. • Use MongoDB and OpenSearch to support application persistence, projections, and search capabilities. • Leverage AI-assisted coding tools (including Copilot CLI, Claude Code, Codex) to accelerate feature delivery, refactor and improve existing code, generate and improve tests, and support documentation. • Actively develop and manage agent-driven coding workflows, where multiple AI agents are used to build, test, and iterate on features in parallel. • Continuously refine code through AI feedback loops, using iterative prompting, validation, and improvement cycles. • Validate and audit AI-generated code for accuracy, security, and maintainability, applying strong engineering judgment and testing discipline. • Collaborate closely with Product, QA, and fellow engineers to deliver high‑quality outcomes. • Participate in code reviews and contribute to engineering standards and best practices. • Mentor and support other engineers as needed through pairing, feedback, and knowledge sharing.
Propio is on a mission to make communication accessible to everyone. As a leader in real-time interpretation and multilingual language services, we connect people with the information they need across language, culture, and modality. We are committed to building AI-powered tools that enhance interpreter workflows, automate multilingual insights, and scale communication quality across industries.
Role Description We are seeking an AI/LLM Safety Engineer to join our AI team and take ownership of how safely our models and agents behave in production; with a focus on AI Safety, Trust & Safety, and Responsible AI. You will design the evaluations that catch unsafe behavior, build the guardrails that stop it, and lead the red-teaming that finds the gaps before our users—or attackers—do. Agent safety is the primary focus of this role: you will help ensure that as our systems gain the ability to call tools and take actions, they do so within well-defined, well-tested boundaries. Key Responsibilities - LLM Safety Evaluation & Red Teaming - Design and maintain a safety evaluation framework—adversarial prompt sets, scenario-based test suites, and regression suites—so that every model and agent update is validated before it ships. - Lead structured red-teaming exercises covering jailbreaks, prompt injection, tool misuse, and data exfiltration; document findings and drive each issue through to remediation and closure. - Guardrails & Runtime Controls - Build and iterate on guardrail logic, including input/output filtering, tool-boundary constraints, action validation, sensitive-data redaction, and policy prompting. - Integrate safety checks into CI/CD and runtime so that unsafe behavior is intercepted before it reaches users. - Agent Safety (primary focus of this role) - Perform threat modeling for agentic scenarios: tool-call boundaries, sandbox isolation, and least-privilege access, with particular attention to preventing agents from exfiltrating data or executing irreversible actions through chained tool calls. - Conduct safety reviews of reinforcement-learning (RL) environments and trajectory data, partnering with environment and agent engineering teams to embed safety constraints directly into the environments themselves. - Monitoring & Observability - Instrument AI features for safety with structured logging, tracing, and metrics, enabling detection of unsafe patterns and regressions in production. - Governance & Collaboration - Prepare evidence for governance reviews—test reports, evaluation summaries, and mitigation validation—aligned with internal Responsible AI standards. - Collaborate with Product and UX to improve safety interactions (warnings, confirmations, refusal messaging, and feedback collection), and align evaluation goals with the Research and Data teams. Qualifications - Bachelor's or Master's degree in Computer Science, Software Engineering, Cybersecurity, or a related technical field—or equivalent practical experience. - 4+ years building production software, with direct experience working on—or securing—ML/LLM systems. - Strong software engineering skills with the ability to write production-grade code (primarily Python), beyond scripting or notebook prototyping. - Solid understanding of LLMs and ML: how models work, prompt engineering, and the safety implications of fine-tuning and RAG (e.g., unsafe retrieval, tool misuse, and data exfiltration). - A security mindset with demonstrated threat-modeling ability; able to threat-model AI workflows and familiar with the fundamentals of access control, data retention, and incident response. - Familiarity with the LLM attack surface—prompt injection, jailbreaks, data poisoning, and supply-chain risk—and working knowledge of the OWASP LLM Top 10. - Hands-on experience with at least one of safety evaluation or red teaming, with the ability to walk through a real finding and how it was remediated. Preferred Qualifications - Hands-on experience with industry safety tooling such as garak, PyRIT, promptfoo, Giskard, and NeMo Guardrails, and the ability to articulate the trade-offs between them. - Visible output in AI safety or security: publications at relevant venues (e.g., the NeurIPS AI Safety Workshop, USENIX Security, or DEF CON AI Village), open-source contributions, or responsible disclosures on frontier models with public write-ups. - Familiarity with AI governance and compliance frameworks (NIST AI RMF, ISO/IEC 42001, EU AI Act) and the ability to translate compliance requirements into concrete engineering tasks. - Engineering experience with agents, RL environments, and/or tool use. - Practical experience with threat-modeling methodologies such as MITRE ATLAS and STRIDE/PASTA. Company Description Propio is on a mission to make communication accessible to everyone. As a leader in real-time interpretation and multilingual language services, we connect people with the information they need across language, culture, and modality. We are committed to building AI-powered tools that enhance interpreter workflows, automate multilingual insights, and scale communication quality across industries.
Woman-owned data management consulting firm helping businesses turn data into actionable insights.
• Define what AI the enterprise will and will not use, on what basis, and under what conditions. • Define what decisions the enterprise is willing to delegate to AI, where humans must remain in the loop, where humans review after the fact, and where AI can act autonomously. • Establish the intake process for AI use cases, including AI impact assessments and risk classification. • Establish who is accountable when AI acts, including model and use case owners in the business, independent risk and compliance review, and audit assurance. • Define how the enterprise governs third-party and vendor AI, including procurement criteria, vendor assessment standards, contractual terms, and ongoing review. • Serve as the primary governance advisor to VP- and Director-level stakeholders; translate strategic intent into actionable governance commitments. • Lead the engagement team and own both the engagement roadmap and engagement deliverables from development to adoption.
Delivering the ultimate Microsoft Azure experience.
• Lead cross-functional engineering and architecture teams in the end-to-end delivery of complex AI systems, ensuring technical excellence and delivery quality. • Define and enforce reference architectures, technical patterns, delivery standards, and accelerators for enterprise AI adoption (leveraging Azure capabilities where appropriate). • Provide senior-level architectural oversight on programs, guiding the design of advanced, distributed, AI-powered applications across multi-system enterprise environments. • Drive the integration of modern AI capabilities—such as generative AI, intelligent/agentic systems, and Model Context Protocol (MCP)—into production-ready software. • Champion best practices for performance, scalability, reliability, observability, and MLOps/LLMOps across all AI delivery engagements. • Serve as a trusted technical advisor to executive stakeholders (CIO, CTO, CDO), helping them define enterprise AI strategies and prioritize high-value opportunities. • Lead technical workshops, architecture reviews, and visioning sessions to align solution direction with business outcomes. • Represent 3Cloud’s technical leadership in client engagements, partner discussions, and industry events, effectively communicating complex AI and engineering concepts. • Oversee a portfolio of large-scale AI delivery programs, ensuring architectural integrity, risk mitigation, and alignment with client strategy. • Provide proactive technical guidance to remove delivery blockers, solve architectural challenges, and ensure teams are building scalable and secure solutions. • Mentor senior architects, tech leads, and engineers, raising the overall technical bar of the practice.
Headquartered in Boston, NineTwoThree partners with established brands and fast-growing startups looking to seize new business opportunities with the clever use of technology. As a product, engineering, design and marketing studio we work to understand your business, unique value proposition and the specific pain points you solve for your users. Our team relentlessly pioneers AI, Web and Mobile solutions to create a competitive advantage for our clients. Since founding the company in 2012 we have worked around the clock to established a track record of reliably creating value and delivering results for our partners and shareholders. With an operating motto of “better software, faster”, the NineTwoThree team has received numerous industry recognitions, including: ***Awards*** • 2024 Top 50 AI firms, alongside the consulting of Microsoft, NVIDIA and IBM. • Top AI agency, • Top Chatbot Agency, • #1 AI Agency in the US, • #3 Machine Learning Agency • #1 Boston AI Consulting Agency • Inc 5000 4 Years In A Row *Top 10 most promising IoT companies by CIO Review
Role Description As an ML Engineer at NineTwoThree AI Studio, you will sit at the intersection of production-grade software engineering, advanced natural language processing, and client delivery. We build custom, high-impact AI systems for brands and startups across diverse industries (such as healthcare, logistics, and fintech). Instead of siloed academic research, this role demands a product-minded builder. You will: - Design, optimize, and deploy robust LLM applications, custom predictive analytics, and agentic workflows directly into our clients' software ecosystems. - Take absolute ownership of features from prototype to production. Technology Stack - Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs. - Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches. - Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines. - Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection). - Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines. - Languages: Production-grade Python. Responsibilities - Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines. - Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment. - Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints. - Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy. - Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection). - Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams. - Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base. Qualifications - 3+ years of experience engineering software with a strong focus on machine learning and natural language processing. - In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. - Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement. - Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. - Exceptional Python coding skills and the ability to query, clean, and structure data efficiently. - Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS. - Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support. Requirements - Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks. - Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built. - Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts. - Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks. - Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience). Benefits - Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years. - Paid sick leave, 10 national holidays, and 2 company days off. - Well-being budget. - Maternity/paternity leave. - Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager). - Hardware upon business needs. - Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor. What's The Process We value your time and ours and make the process fast and easy. Our interview process takes the following steps: - A short interview with the HR. - 2nd technical interview with ML Engineer and CTO (optional). - 3rd live-coding interview. - Offer.
• Architect and build scalable multi-agent systems • Own backend services, APIs, and data pipelines • Collaborate with Product, Design, and Client teams • Continuously improve existing architecture and reduce operational complexity
• Architect and ship production AI systems end-to-end (orchestration, retrieval, inference, evaluation, and monitoring) for clinical decision support, document understanding, and agentic medical companions. • Design agentic workflows with custom skills and harnesses, using frontier LLMs and self-hosted models where appropriate, and resisting the urge to reach for an agent when a function call would do. • Lead development of structured information extraction pipelines from clinical documents, including automated verification systems and human-in-the-loop gates, where necessary. • Build robust evaluation infrastructure (golden datasets, LLM-as-judge, regression tests, online A/B evaluation) that gates every model and prompt change. Accuracy, safety, cost, and latency are first-class metrics. • Engineer for latency and cost: streaming, caching, prompt compression, model routing, and inference-cost budgets. • Own production health: trace- and span-level observability, prompt versioning, drift detection, cost monitoring, replay-from-production for debugging, PHI-safe logging throughout, and the general art of finding out something is broken before a clinician does. • Operationalize models trained by the AI Scientist team: serving, evaluation in production, rollback paths, and the feedback loops that turn real usage into training and eval data, rather than into a Slack thread nobody reads. • Partner with clinicians, the data engineering team, and our research collaborators to translate clinical requirements into specifications and deliverable systems. • Educate and mentor PMs and engineers across Atria on applied AI best practices, from prompt and evaluation design to choosing the right model for the job (which, surprisingly often, is not the largest one). • Set technical direction for AI engineering at Atria: architecture decisions, build-vs-buy calls, and the evaluation and observability standards the team actually works to. • Raise the bar on engineering practices, code review, observability, and incident response. Quietly, persistently, and with grace. • Drive vendor and partner technical due diligence for AI/ML vendors: BAA scope, PHI handling, sub-processor obligations, and IP terms. • Mentor other AI engineers through code review, design docs, and architecture decisions, with the patient conviction that good systems are made twice: once badly, then properly.
Role Description As a Senior AI Engineer at Atria, you will own the design, development, and production deployment of the LLM-powered systems that put AI directly in front of clinicians and members: - Agentic medical companions - Clinical decision support - Document understanding pipelines that turn unstructured clinical data into usable signal This is a hands-on, high-ownership role for an engineer who wants to do work with direct patient impact. What you'll do - Architect and ship production AI systems end-to-end (orchestration, retrieval, inference, evaluation, and monitoring) for clinical decision support, document understanding, and agentic medical companions. - Design agentic workflows with custom skills and harnesses, using frontier LLMs and self-hosted models where appropriate. - Lead development of structured information extraction pipelines from clinical documents, including automated verification systems and human-in-the-loop gates, where necessary. - Build robust evaluation infrastructure (golden datasets, LLM-as-judge, regression tests, online A/B evaluation) that gates every model and prompt change. - Engineer for latency and cost: streaming, caching, prompt compression, model routing, and inference-cost budgets. - Own production health: trace- and span-level observability, prompt versioning, drift detection, cost monitoring, replay-from-production for debugging, PHI-safe logging throughout. - Operationalize models trained by the AI Scientist team: serving, evaluation in production, rollback paths, and feedback loops that turn real usage into training and eval data. - Partner with clinicians, the data engineering team, and research collaborators to translate clinical requirements into specifications and deliverable systems. - Educate and mentor PMs and engineers across Atria on applied AI best practices. Leadership and Influence - Set technical direction for AI engineering at Atria: architecture decisions, build-vs-buy calls, and evaluation and observability standards. - Raise the bar on engineering practices, code review, observability, and incident response. - Drive vendor and partner technical due diligence for AI/ML vendors: BAA scope, PHI handling, sub-processor obligations, and IP terms. - Mentor other AI engineers through code review, design docs, and architecture decisions. - A bias to ship: prefer a rough thing in production tomorrow over a beautiful thing in a design doc next quarter. - A scrappy streak: pick up an unfamiliar tool, framework, or clinical concept quickly. - A serious drive to keep getting better: read others' code, papers, and post-mortems. Qualifications - Hands-on experience building and operating LLM-powered or production ML systems: 4+ years. - Strong Python skills and deep familiarity with at least one production backend framework (FastAPI, Flask, etc.). - Hands-on experience with LLM orchestration, function/tool calling, and retrieval system design. - Working knowledge of a cloud data warehouse (Snowflake strongly preferred) and modern data tooling such as dbt, Dagster, or Airflow. - A track record of designing evaluation systems for LLM applications. - Familiarity with safety considerations for LLM applications. - Practical understanding of HIPAA, BAAs, PHI handling, and working in a regulated environment. - Strong written communication: design docs, technical RFCs, and documentation. Nice to have - Experience in healthcare, clinical informatics, or other regulated domains. - Familiarity with medical ontologies (LOINC, SNOMED CT, ICD-10, RxNorm) and clinical data standards (FHIR, HL7). - Experience with self-hosted model serving and inference optimization. - Multimodal experience: vision, structured data, or time-series data alongside text. Salary and Benefits - Salary range: $190,000 - $250,000 + performance-based bonus - Excellent health and wellness benefits, fully covered by Atria, effective date of hire - OneMedical membership for employees & dependents, giving access to 24/7 virtual care - Fertility & family planning - Company-covered preventive health screenings through partner hospitals - Fitness Perks, including Wellhub + - 401k contributions and 4% match starting after 6 months - Flexible Time Off - Continuing medical education (CME) and CEU support for professional licensure
• Design and build production-grade LLM-powered agents and workflows within Smartsheet, including architecture decisions on system design, data pipelines, and deployment strategies • Develop and optimize prompts, RAG pipelines, and agent reasoning patterns; build evaluation frameworks to measure accuracy, hallucination rates, and performance across model versions • Implement and manage end-to-end AI systems on cloud infrastructure (AWS, GCP, or Azure), including monitoring, optimization, and incident response • Collaborate with Engineering, Product, and cross-functional teams to translate business requirements into technical AI solutions that drive real impact • Mentor and guide other engineers on applied ML best practices, modern AI development patterns, and production considerations • Establish best practices and standards for AI development, model governance, and responsible AI within the organization • Stay current with the rapidly evolving AI/LLM landscape and evaluate emerging tools, frameworks, and techniques for Smartsheet's needs
Assured is a claims automation insurtech backed by leading Silicon Valley investors.
• Build agentic workflows – Design and ship internal agents -or entire orchestrated agentic workflows- on Claude for research, account briefs, objection prep, content drafting, and named-account monitoring – or any other use case we consider valuable. • Build the data model – Stand up a warehouse-backed marketing data model (e.g., Snowflake) that stitches all data sources into one source of truth. • Ship the GTM/Marketing dashboards – Build and maintain the weekly data dashboards that the CEO, Head of Marketing and Head of Sales read in under five minutes. • Integrate the intelligence stack – Wire up product marketing to ABM, campaigns to execution, GTM strategy to pipeline. • Be the force multiplier – Give every other hire – product marketing, integrated growth, community – the data, tooling, and automation they need to move faster. • Keep it compliant and observable – Build inside carrier compliance norms and make the system’s health and pipeline legible at a glance.
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Cloud, Python, AWS, Azure, Angular, DynamoDB
