Sr. Software Engineer - AI Platforms and Automation

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 Sr. 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 that help teams create, manage, and deliver high-quality clinical data. The ideal candidate is a strong software engineer who enjoys owning complex systems, improving reliability, and partnering across technical and business teams to evolve AI-enabled solutions. WHAT YOU’LL DO: Own and enhance internal platforms Maintain and enhance internally developed applications and tooling that supports terminology management, content creation, mapping, workflow automation, and content delivery. Build and maintain integrations across internal applications, APIs, knowledge bases, databases, and AI services. Contribute to the design and implementation of new automation and AI-enabled capabilities as business needs evolve. Support reliable production systems Own operational support for AI-enabled applications and workflows in production, including troubleshooting, incident response, release coordination, and ongoing maintenance. Manage application deployments, infrastructure configuration, monitoring, alerting, and operational support for AWS-hosted applications and services. Investigate production issues, perform root-cause analysis, and implement durable solutions that improve reliability. Enable AI-powered workflows Support AI agents and workflow automation capabilities as they mature from pilot initiatives into scalable production solutions. Develop and troubleshoot cloud-based workflows using AWS services such as Bedrock, Lambda, Glue, S3, IAM, CloudWatch, and MWAA/Airflow. Implement testing, monitoring, and operational readiness practices that improve the quality and reliability of AI-enabled workflows. Collaborate across teams Partner with clinical, terminology, product, data science, and engineering teams to improve AI-enabled workflows while maintaining appropriate human review and auditability. Mentor team members and promote software engineering best practices for secure, maintainable, and production-ready systems. WHAT YOU’LL NEED: Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or a related technical field; equivalent professional experience will also be considered. 7+ years of professional experience in software engineering, backend engineering, platform engineering, DevOps, MLOps, cloud engineering, or a related discipline, including experience supporting production systems. Strong proficiency in Python and experience building maintainable services, APIs, internal tools, jobs, or workflow automation in production environments. Experience designing, deploying, and supporting cloud-based applications in AWS environments, including hands-on experience with services such as Amazon Bedrock, Lambda, S3, IAM, CloudWatch, Glue, MWAA/Airflow, or similar technologies. Experience with CI/CD pipelines, Git-based development workflows, automated testing, configuration management, and release practices. Experience with Docker, Kubernetes or other containerized services, Terraform or Infrastructure-as-Code, and production monitoring/alerting tools. Experience with workflow orchestration, data pipelines, or job scheduling tools such as Airflow/MWAA, Glue, Lambda, cron-based jobs, or equivalent technologies. Working knowledge of SQL and relational databases such as PostgreSQL; experience with distributed data or search systems is a plus. Experience building or supporting AI-enabled applications using LLM APIs, retrieval-augmented generation (RAG), knowledge bases, AI agents, or similar technologies. Strong troubleshooting skills, including production issue triage, root-cause analysis, log analysis, and implementation of durable solutions. Ability to partner effectively with domain experts and translate workflow needs into practical, maintainable technical solutions. Strong communication, documentation, and collaboration skills in cross-functional environments. PREFERRED QUALIFICATIONS: Experience scaling AI-enabled applications or agent-based workflows from prototype or pilot phases into reliable production systems. Experience with modern AI development practices, including tool/function calling, prompt and context engineering, MCP patterns, and AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor. Experience with AI application frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies. Experience designing or supporting AI evaluation frameworks, quality monitoring practices, or human-in-the-loop workflows for AI-assisted outputs. Experience in healthcare technology, clinical data, clinical terminology, content curation, or other regulated data environments. Familiarity with knowledge graph technologies and semantic standards such as RDF, OWL, SPARQL, SHACL, FHIR, SNOMED CT, LOINC, RxNorm, ICD-10, CPT, or related healthcare standards. Experience working with vector databases, embeddings, search technologies, or other retrieval-based AI architectures. AWS certifications such as AWS Certified Solutions Architect, AWS Certified Machine Learning – Specialty, or AWS Certified Generative AI Developer – Professional.