AI Engineer

Terumo BCT, Inc.•Denver, CO

9d•$126,800 - $158,500•Onsite

About The Position

We are seeking a motivated AI Engineer who, working under general direction, designs, builds, and operates AI-enabled capabilities in production environments. Responsible for implementing reliable AI services, products, and workflows, integrating with enterprise systems, and applying practical engineering discipline to deliver secure, maintainable solutions under real constraints.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, or related field; or equivalent education and experience sufficient to successfully perform the essential functions of the job may be considered.
Minimum of 3 years of demonstrated experience building and operating production-grade software services, including exposure to AI/ML or LLM-based capabilities.
Hands-on experience deploying and supporting solutions in production environments (Azure preferred) that integrate with enterprise systems and data sources.
Experience with modern engineering practices, including source control, automated testing, and release management.
Strong debugging and systems-thinking skills; ability to diagnose issues across service boundaries.
Practical ability to deploy and operate AI/ML or LLM-based systems in real environments.
Familiarity with MLOps or production support practices, such as versioning, evaluation signals, monitoring, and safe rollout patterns.
Ability to make disciplined tradeoffs across scope, timelines, and operational risk.
Clear written and verbal communication skills; effective collaboration across technical and non-technical teams.

Nice To Haves

Exposure to: Medical device or healthcare industry (internship, course, lab, project)
Data platforms (Spark, Databricks, Hadoop)
Experience with agent-based architecture or conversational AI platforms
Understanding of regulated software concepts (e.g., GxP, ISO 13485, ISO 42001, MDSAP, IEC 62304, SaMD guidance)
Familiarity with responsible AI, model interpretability, or bias mitigation
Strong analytical and structured problem-solving skills.

Responsibilities

Designs and implements AI-enabled services, products, and workflows, taking use cases from POC and prototype through production release and maintenance.
Develops and maintain software components required to integrate AI capabilities into applications and operational workflows.
Establishes reliable delivery practices for AI services, including automated build/release patterns and operational readiness.
Monitors system behavior and performance, troubleshooting issues and driving improvements to reliability, latency, and cost.
Applies practical MLOps practices appropriate to the solution (versioning, evaluation, monitoring signals, and safe rollout patterns).
Reviews and improves code quality (including AI-assisted code) to ensure correctness, security, and maintainability.
Partners with product and cross-functional stakeholders to translate requirements into deliverable scope, surfacing tradeoffs early and keeping work unblocked.
Documents system behavior, operational procedures, and key design decisions to support long-term supportability.
Follow medical device quality system processes, documentation standards, and audit expectations.
Support AI risk assessment, validation, traceability, and performance monitoring activities.
Contribute to explainability, bias evaluation, and safety controls.
Document best practices and key learnings to support organizational knowledge sharing.
Partners and collaborates with business stakeholders, Digital Transformation team, ITS team members, and Terumo Corporate associates to deliver enterprise and corporate solutions
Document methods, architecture decisions, and model evaluations.
Engage in continuous learning on medical device regulations, AI governance, and emerging AI technologies.
Mentor other team members and contribute to a collaborative, growth-oriented team culture.
Participates in evaluation of tools and approaches that improve engineering productivity and operational reliability of AI systems.
Contributes to shared engineering standards and best practices across AI projects.
Supports knowledge sharing through documentation, demos, education events, and peer collaboration.