AI Engineer

Foundation Medicine

6d•Hybrid

About The Position

The AI Engineer supports the day-to-day design, operation, and continuous improvement of enterprise AI platforms, applications, and agentic workflows. The role combines hands-on engineering, applied solution delivery, and cross-functional collaboration to establish secure, scalable, governed, and practical AI capabilities for business processes. Work may span platform configuration, integrations, governed data access, retrieval pipelines, evaluation, deployment, monitoring, and continuous improvement. The AI Engineer collaborates with Cybersecurity, Infrastructure, Data, Operations, and business stakeholders to translate AI capabilities into secure and compliant business outcomes.

Requirements

Bachelor's degree and at least 2 years of experience designing, building, and operating production software platforms or cloud solutions on Azure and/or AWS, including at least 2 years developing and supporting LLM-powered applications in production environments.
Strong software engineering and cloud platform expertise, including Python, APIs, CI/CD, containers, Terraform infrastructure as code, version control, observability, and secure deployment practices.
Hands-on experience designing enterprise AI solutions, including prompt engineering, structured outputs, tool calling, context engineering, evaluation frameworks, agentic workflows, and production AI application delivery.
Demonstrated experience implementing retrieval-augmented generation (RAG) and enterprise knowledge solutions, including content ingestion, embeddings, vector search, re-ranking, grounding strategies, and evaluation of AI-generated outputs.
Experience integrating AI platforms with enterprise systems and governed data, including authentication and authorization patterns, MCP or equivalent tool integration frameworks, APIs, OAuth, and secure access controls.
Strong knowledge of enterprise AI, cloud, and security platforms, including Azure AI Foundry, Azure OpenAI Service and/or AWS Bedrock, Entra ID, managed identities, IAM, Conditional Access, and related governance and compliance controls.
Ability to work effectively with business, data, security, and technology stakeholders, translating requirements into practical solutions within governance, risk, compliance, and operational guardrails.

Nice To Haves

Experience developing or operating AI, data, or software platforms in regulated industries such as healthcare, biotechnology, genomics, pharmaceuticals, or financial services.
Familiarity with regulatory and compliance frameworks including HIPAA, GxP, 21 CFR Part 11, validated systems, audit trails, and emerging AI regulations.
Experience with AWS Bedrock, Azure AI Foundry, Anthropic Claude, Bedrock AgentCore, Claude Code, or similar enterprise AI technologies.
Familiarity with AI governance, evaluation, and risk management frameworks such as NIST AI RMF.
Experience with fine-tuning, distillation, open-weight models, or cost- and latency-optimized AI deployments.
Contributions to open-source AI tooling, agent frameworks, MCP servers, or evaluation frameworks.
Relevant Microsoft or AWS certifications.
Demonstrated commitment to responsible AI, security, and compliance.
Understanding of HIPAA and the importance of patient data privacy.
Commitment to reflect FMI’s values: Integrity, Courage, and Passion.

Responsibilities

Design, operate, and continuously improve enterprise AI platforms, services, integrations, and governance capabilities.
Administer and support AI platforms, agent services, and enterprise controls, including identity and access management, observability, provisioning, lifecycle management, and governance workflows.
Implement security controls, auditing capabilities, data protection measures, and compliance guardrails in partnership with Cybersecurity, Data, and Infrastructure teams.
Design and develop secure integrations enabling AI applications and agents to interact with approved enterprise systems, tools, and governed data assets.
Scope, build, deploy, and support AI applications, copilots, and agentic workflows that meet business needs within technical and governance guardrails.
Develop and maintain retrieval-based and tool-enabled AI solutions using approved knowledge sources and governed data assets to support accurate, traceable, and reproducible outcomes.
Establish and execute evaluation, monitoring, testing, and continuous improvement practices to ensure AI solutions remain effective, reliable, and fit for purpose.
Maintain production engineering practices, including code quality, deployment pipelines, instrumentation, version control, operational readiness, and platform reliability.
Provide design guidance, reusable patterns, reference implementations, documentation, and practical support to enable internal builders and stakeholders.
Comply with FMI's attendance policies.