Note: The job is a remote job and is open to candidates in USA. MOBE helps people discover new ways to live healthier through evidence-based health guidance and pharmacist-led medication management. They are seeking an AI Engineer to build production AI systems that translate healthcare data into actionable outputs, focusing on AI engineering, machine learning operations, and healthcare analytics.
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
- Design AI evaluation practices: 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
- Support cloud-based AI infrastructure: 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
Skills
- 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
- 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
Benefits
- Paid time off
- Medical, dental, and vision insurance
- Life and disability insurance
- 401(k) with company match
- Tuition reimbursement
- Additional benefits available to eligible employees
Company Overview