Senior AI/ML Engineer, Applications & Automation

Job description

At IMO Health, we combine strengths in software development, artificial intelligence, and clinical expertise to create AI-driven solutions that enhance access to reliable health information, support clinical decision-making, and improve patient outcomes.  We are looking for a Senior Software Engineer to own and evolve the internal software platforms that support IMO Health's terminology and knowledge graph initiatives. This role will maintain and enhance production applications, APIs, integrations, and AI-enabled workflows while helping introduce new AI capabilities into existing business processes as the platform continues to evolve.  The ideal candidate is a software engineer first—someone who enjoys owning and evolving production software while applying AI technologies to solve real business problems. As the platform continues to evolve, you'll help introduce new AI-enabled capabilities into existing business workflows while ensuring the underlying systems remain reliable, scalable, and production-ready.

WHAT YOU’LL DO: - Develop machine learning models, agents, and automation workflows for terminology management, content creation, mapping, and validation — evolving them from experimentation into scalable production systems. - Build agentic workflows that use LLMs, tools, APIs, knowledge sources, retrieval capabilities, and structured business rules to complete complex tasks. - Build and maintain retrieval-augmented generation solutions, vector and semantic search capabilities, and prompt and context-management strategies. - Partner with our data science team to understand, integrate, and productionize their existing agents, and bring your own model and agent development to the team's roadmap. - Own the deployment, monitoring, troubleshooting, and continuous improvement of AI workflows in production, including root-cause analysis and durable remediation of failures or unexpected outputs. - Design evaluation, testing, and observability practices for AI systems, and implement controls for auditability, explainability, and human-in-the-loop review in clinically sensitive workflows. - Develop cloud-based solutions using AWS services such as Amazon Bedrock, SageMaker, and Lambda, applying CI/CD, containerization, automated testing, and secure development practices. - Work closely with clinical, mapping, product, data science, and engineering partners to translate workflows into practical solutions — and help define where AI automation is appropriate, where deterministic logic is required, and where human review must remain.

WHAT YOU’LL NEED:

-   5+ years across AI/ML engineering, data science, machine learning engineering, or related disciplines, with a foundation in applied machine learning.

- Hands-on experience building agents and agentic workflows, including orchestration and tool or function calling.

- Hands-on experience building RAG solutions, including embeddings, vector databases, semantic search, and context engineering.

- Hands-on MLOps experience taking models and agents into production — deployment, versioning, monitoring, and CI/CD across multiple environments.

- Strong Python proficiency and experience developing maintainable services, APIs, pipelines, or workflow automation, plus working knowledge of SQL and relational databases such as PostgreSQL.

- Experience with cloud-based AI infrastructure, preferably AWS and Amazon Bedrock.

- Strong troubleshooting and root-cause analysis skills, and the ability to partner with domain experts and convert ambiguous workflow needs into scalable technical solutions.

- Clear written and verbal communication in cross-functional environments.

PREFERRED QUALIFICATIONS:

- LangChain or LangGraph, LlamaIndex, OpenSearch, vector databases, or evaluation frameworks.

- Multi-agent or tool-using workflows, including state management, memory, routing, and failure recovery.

- Testing and evaluation approaches for non-deterministic AI systems.

- Healthcare technology, clinical terminology, clinical data normalization, mapping workflows, or regulated data environments.

- Familiarity with healthcare data standards such as knowledge graphs, FHIR, SNOMED CT, LOINC, RxNorm, ICD-10, or CPT.

- AI solutions incorporating human review, auditability, explainability, and quality governance.