Sr. Software Engineer - AI Platforms & Automation

Imo Online; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

IMO Health is seeking a Senior Software Engineer to own and evolve the internal software platforms that support terminology management, content creation, mapping, and AI-enabled workflows. You will maintain production applications, APIs, integrations, and AI services while introducing new AI capabilities into business processes.

You will collaborate with clinical, product, data science, and engineering teams to ensure reliability, scale, and auditability across AWS-hosted systems, mentoring

Qualifications

• 7+ years of experience in software engineering, backend/platform engineering, DevOps, or related field.

Responsibilities

• Own and evolve internal platforms for terminology management, content mapping, automation, and tooling.

Skills

Python

AWS

CI/CD

AI-enabled workflows

LLM APIs

LangChain

LlamaIndex

RAG

PostgreSQL

Knowledge graphs

Education

Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or related field

Tools

Docker

Kubernetes

Terraform

Airflow

MWAA

S3

AWS Lambda

Glue

Job description

At IMO Health, we combine strengths in software development, artificial intelligence, and clinicalexpertiseto 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.

Own and enhance internal platforms

• Maintain and enhance internally developed applications and tooling that support 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 needsevolve.

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.

• 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, ora relateddiscipline, including experience supporting production systems.
• Strongproficiencyin 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.
• Experience building or supporting AI-enabled applications using Amazon Bedrock, LLM APIs, knowledge bases, AI agents, retrieval-augmented generation (RAG), 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; experiencewith 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.
• 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 prompt engineering, tool/function calling, AI evaluation techniques, and AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor.
• Experience with AI application frameworks such asLangChain,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.