• Architect, prototype, and deploy RAG pipelines, combining vector search, hybrid retrieval, reranking and contextual compression techniques. • Contribute to design and orchestration of multi-agent LLM systems using community frameworks and custom orchestration layers. • Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data. • Partner with cross-functional (product, infra, data engineering, and software engineering) to build robust, high-scale systems that underlie all of our data processing and ML Operations.

Position: Strategic Account Executive, Cloud & AI Platform Partnerships Job Description: What You’ll Be Doing - Lead the technical and strategic alignment of Arrow’s externally available platforms, primarily ArrowSphere-aaS and Arrow AI 360, to accelerate commercial growth - Drive go-to-market acceleration by engaging with Telcos, MSPs, Distributors, and Digital Service Providers to increase platform adoption and expand pipeline - Deliver high-impact executive workshops, demos, and transformation sessions that position ArrowSphere as a leading marketplace platform and AI 360 as an AI expense and performance management solution - Partner with regional leadership to support pipeline development, forecasting, and long-term planning across strategic accounts and partners - Lead strategic transformation programs with existing ArrowSphere customers, enabling adoption of new platform capabilities and scaling their cloud business - Collaborate cross-functionally with marketing, product, and engineering teams to design and execute go-to-market motions and accelerate platform evolution - Contribute to platform strategy, including partner segmentation, acquisition, and revenue growth initiatives - Provide market and partner feedback to help shape product roadmap and platform innovation, ensuring alignment with customer needs and market trends - Influence and support the AI 360 go-to-market and product strategy, including ecosystem development, partner prioritization, and use case expansion - Develop and scale frameworks, playbooks, and enablement materials that drive partner maturity and accelerate sales cycles - Act as a thought leader and evangelist, representing Arrow at industry events, executive forums, and partner engagements - Serve as a trusted advisor to partners navigating cloud modernization, marketplace adoption, and AI-driven transformation initiatives - Align platform capabilities with partner and vendor strategies to drive measurable business outcomes and growth What We’re Looking For - 12+ years of experience in cloud, platform, or technology solutions, spanning sales, consulting, product, or partner ecosystem roles - Strong understanding of cloud marketplaces, SaaS models, and platform-based business strategies - Experience working with or supporting Telcos, MSPs, Distributors, or large partner ecosystems - Proven ability to lead customer-facing engagements, including executive presentations, workshops, and strategic discussions - Ability to operate at both strategic and execution levels, influencing direction while driving tangible outcomes - Experience collaborating across sales, marketing, product, and engineering teams in a matrixed environment - Strong business acumen with the ability to build compelling value propositions and ROI-driven narratives - Exposure to or experience with AI, data platforms, or emerging technologies is a plus - Excellent communication and presentation skills, with the ability to simplify complex technical concepts for diverse audiences - Self-starter with a proactive, consultative mindset and the ability to manage multiple priorities - Willingness to travel as needed Work Arrangement: Fully Remote: Must be able to travel to an Arrow office location as requested by Arrow leadership. What’s In It For You : At Arrow, we recognize that financial rewards and great benefits are important aspects of an ideal job. That’s why we offer competitive financial compensation, including various compensation plans and a solid benefits package. - Medical, Dental, Vision Insurance - 401k, With Matching Contributions - Short-Term/Long-Term Disability Insurance - Health Savings Account (HSA)/Health Reimbursement Account (HRA) Options - Paid Time Off (including sick, holiday, vacation, etc.) - Tuition Reimbursement - Growth Opportunities - And more! #LI-MV1 Annual Hiring Range/Hourly Rate: $191,300.00 - $308,006.16 Actual compensation offer to candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level. The pay ratio between base pay and target incentive (if applicable) will be finalized at offer. Location: US-CO-Colorado (Remote Employees) Remote work employees may be required to be present at the closest designated Arrow office for work-related purposes, at the Company’s request and sole discretion. Time Type: Full time Job Category: Sales EEO Statement: Arrow is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, gender, age, sexual orientation, gender identity, national origin, veteran or disability status. (Arrow EEO/AAP policy) All Arrow job postings are for existing job vacancies. We anticipate this requisition will be open for a minimum of five days, though it may be open for a longer period of time. We encourage your prompt application. In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.

About the role We're looking for an AI Engineer who is equal parts builder and thinker — someone who gets energized by turning complex ideas into production-ready AI systems that actually work at scale. This role sits at the intersection of machine learning, software engineering, and practical business impact. You'll design, develop, and deploy end-to-end AI solutions — with a strong emphasis on LLM-powered applications, intelligent automation, and GenAI integrations. You won't just experiment in notebooks; you'll ship to production, own the lifecycle, and collaborate closely with cross-functional teams to ensure your work drives measurable outcomes. What you'll do - Design and build production-grade AI/ML systems, including LLM-powered pipelines, RAG architectures, and agentic AI workflows - Develop and maintain scalable MLOps pipelines covering model training, deployment, monitoring, and lifecycle management - Integrate AI capabilities into enterprise platforms and workflows using tools like LangChain, Autogen, OpenAI API, and Hugging Face - Collaborate with data scientists, product stakeholders, and software engineers to translate prototypes into reliable, scalable solutions - Leverage cloud platforms (AWS, Azure, or GCP) to build and scale AI infrastructure and services - Create clear technical documentation for AI models, systems, and workflows - Stay current on emerging AI frameworks What you bring - 3–7 years of experience in AI/ML engineering with hands-on deployment experience (not just research or prototyping) - Strong proficiency in Python and familiarity with frameworks such as TensorFlow, PyTorch, or Keras - Practical experience with LLMs, prompt engineering, and GenAI application development - Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes) - Solid understanding of MLOps principles, CI/CD workflows, and model governance - Strong communication skills — you can explain AI concepts clearly to both technical and non-technical audiences - Bachelor's or Master's degree in Computer Science, Engineering, Applied Mathematics, or a related field — or equivalent practical experience - Experience with Agentic AI frameworks (LangGraph, Autogen, CrewAI) - Contributions to open-source AI projects or published research - Familiarity with infrastructure-as-code tools (Terraform, Bicep) - Experience deploying AI in regulated industries such as healthcare, finance, or legal Why this role - Fully remote — no commute, no office mandates, full flexibility to do your best work from home - Work on meaningful, real-world AI applications with direct business impact — not just internal tools - Competitive compensation benchmarked to Atlanta market rates with room to grow - Collaborative, low-bureaucracy environment where engineers have a voice in technical direction - Access to the latest AI tooling and encouragement to experiment and innovate PeopleLift is an equal opportunity employer and staffing partner. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, or any other characteristic protected under applicable federal, state, or local law. We are committed to building diverse, inclusive teams and encourage candidates from all backgrounds to apply. This employer participates in E-Verify. Applicants have rights under Federal Employment Laws. Our client is an EEOC Employer and encourages all minority groups to apply. By applying to this job, as part of our typical recruiting process, from time to time, we may contact you regarding positions that we feel are a good fit for you or engage with you during the recruiting process via SMS text message. Message and data rates may apply, depending on your mobile phone service plan. At any time you can get more help by replying HELP to these texts, or you can opt-out completely by replying STOP. Our Terms of Service are available at www.peoplelift.com.

Job Title: Full-Stack AI Engineer Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for model deployments, experimentation cycles, and sprint schedules) About the Role: Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications. This role requires bridging software engineering with applied machine learning, ensuring that models are integrated into production systems that are scalable, reliable, and user-friendly. The Full-Stack AI Engineer combines back-end services, front-end interfaces, and machine learning pipelines to deliver practical, business-driven AI solutions. Responsibilities: AI Model Integration: - Deploy pre-trained and fine-tuned ML/LLM models (OpenAI, Hugging Face, TensorFlow, PyTorch). - Wrap models in APIs (FastAPI, Flask, Node.js) for scalable inference. - Implement vector search integrations (Pinecone, Weaviate, FAISS) for retrieval-augmented generation (RAG). Data Engineering & Pipelines: - Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data. - Automate data labeling, preprocessing, and versioning with Airflow, Prefect, or Dagster. - Store and manage datasets in cloud warehouses (Snowflake, BigQuery, Redshift). Application Development (Full-Stack): - Build front-end UIs in React, Next.js, or Vue to surface AI-powered features (chatbots, dashboards, analytics). - Design back-end services and microservices to connect models to business logic. - Ensure responsive, intuitive, and secure interfaces for end users. Infrastructure & Deployment: - Containerize ML services with Docker and deploy to Kubernetes clusters. - Automate CI/CD pipelines for model updates and application releases. - Monitor latency, cost, and model drift with MLflow, Weights & Biases, or custom dashboards. Security & Compliance: - Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2). - Implement rate limiting, access control, and secure API endpoints. Collaboration & Iteration: - Work with data scientists to productionize prototypes. - Partner with product teams to scope AI features aligned with business needs. - Document systems for reproducibility and knowledge transfer. What Makes You a Perfect Fit: - Strong coder with a foundation in both full-stack development and applied ML/AI. - Comfortable building prototypes and scaling them to production-grade systems. - Analytical problem solver who balances performance, cost, and usability. - Curious and adaptable, staying current with emerging AI/LLM tools and frameworks. Required Experience & Skills (Minimum): - 3+ years in software engineering with exposure to AI/ML. - Proficiency in Python (PyTorch, TensorFlow) and JavaScript/TypeScript (React, Node.js). - Experience deploying ML models into production systems. - Strong SQL and experience with cloud data warehouses. Ideal Experience & Skills: - Built and scaled AI-powered SaaS products. - Experience with LLM fine-tuning, embeddings, and RAG pipelines. - Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker). - Familiarity with microservices, serverless architectures, and cost-optimized inference. What Does a Typical Day Look Like? A Full-Stack AI Engineer’s day revolves around connecting models to real-world applications. You will: - Review and refine model APIs, testing latency and accuracy. - Write front-end code to surface AI features in user-friendly interfaces. - Maintain pipelines that clean and prepare new datasets for training or fine-tuning. - Deploy updates through CI/CD pipelines, monitoring cost and performance post-release. - Collaborate with product and data science teams to prioritize AI features that solve real user problems. - Document workflows and results so solutions are repeatable and scalable. In essence: you ensure AI moves from prototype to production — reliable, compliant, and impactful. Key Metrics for Success (KPIs): - Successful deployment of AI features to production on schedule. - Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints. - Reduction in manual workflows replaced by AI features. - Model performance tracked and stable (accuracy, drift, false positives/negatives). - Positive user adoption and satisfaction of AI-driven features. Interview Process: - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration) - Client Interview(s) with Engineering Team - Offer & Background Verification