AI Engineer-AI Platform

Responsibilities

• Build production AI and RAG systems: Design RAG pipelines over enterprise data with attention to retrieval quality, grounding, latency, source traceability, privacy, and answer reliability.
• Create repeatable methods to measure retrieval quality, coverage, hallucination risk, groundedness, latency, regression, and user-facing confidence.
• Develop agentic workflows: Build controlled AI workflows that retrieve context, call tools, execute defined steps, and support decision processes with observability and guardrails.
• Operationalize ML and AI lifecycle management: Use MLflow or equivalent tooling for experiment tracking, model registry, lineage, reproducibility, deployment, and monitoring.
• Deploy and monitor models or AI services using AWS SageMaker, Bedrock, Airflow, feature-store patterns, or equivalent production-ready tooling.
• Build AI-ready data pipelines: Use Python, SQL, dbt, Airflow, and cloud services to support model training, RAG ingestion, feature generation, batch inference, and AI application workflows.
• Integrate outputs into reporting and BI: Ensure ML and AI outputs are trusted, documented, interpretable, and consumable through reporting hubs, dashboards, semantic layers, APIs, or downstream workflows.
• Uphold governance and data protection: Ensure AI systems follow privacy, security, quality, auditability, and compliance standards when handling PHI, PII, and confidential information.
• Translate business questions into AI solutions: Partner with Data Science, Analytics Engineering, Medical Economics, DataOps, TechOps, and business stakeholders to convert ambiguous problems into measurable solutions.

Requirements

• 5–7 years of experience in AI engineering, ML engineering, applied machine learning, data science engineering, or related platform-oriented roles.
• Strong proficiency in Python and SQL for data processing, pipeline development, model workflows, and AI application logic.
• Hands-on experience building or operating LLM-powered systems, including RAG, AI assistants, agentic workflows, or AI-enabled data products.
• Practical experience with RAG architecture, including ingestion, chunking, embeddings, retrieval, grounding, and evaluation.
• Experience taking ML or AI systems from prototype to production, including deployment, monitoring, observability, and iteration.
• Experience with MLOps tooling such as MLflow, model registries, experiment tracking, feature stores, model monitoring, or lifecycle governance.
• Experience with orchestration tools such as Apache Airflow or equivalent workflow systems.
• Strong foundation in data engineering concepts, including data modeling, testing, lineage, quality checks, CI/CD, and version-controlled development.
• Ability to work responsibly with sensitive or regulated data and apply privacy, security, governance, and auditability standards.
• Ability to collaborate with clinical, business, analytics, and engineering stakeholders to turn unclear needs into practical AI solutions.

Preferred

• Experience with AWS AI/ML services such as SageMaker, Bedrock, Lambda, S3, ECR, EventBridge, or related services.
• Experience with AI-assisted development tools such as Claude Code, Copilot, Aider, Cursor, or similar workflows.
• Experience with agentic frameworks or orchestration patterns such as LangGraph, CrewAI, Copilot agents, MCP/tool-based architectures, or equivalent approaches.
• Experience with dbt, semantic layers, BI/reporting tools, Dash, QuickSight, Lightdash, Tableau, Looker, or custom reporting hubs.
• Experience with Docker, Kubernetes, GitLab/GitHub CI/CD, Terraform, infrastructure-as-code, or feature-store architectures.
• Healthcare, pharmacy, claims, care management, clinical analytics, or regulated-data experience.
• Experience building AI systems over claims, care plans, medication data, transcripts, clinical notes, or operational healthcare documentation.
• Experience with human-in-the-loop workflows, structured + unstructured retrieval, model drift detection, AI governance, or domain-specific model adaptation.
• Experience fine-tuning or adapting large language models for domain-specific use cases.