Machine Learning Engineer in Denver
Job in
Denver, Denver County, Colorado, 80285, USA
Listed on 2026-02-28
Listing for:
TalentBridge
Full Time
position Listed on 2026-02-28
Job specializations:
-
IT/Tech
Machine Learning/ ML Engineer, AI Engineer, Data Engineer, Cloud Computing
Job Description & How to Apply Below
Role: Senior Machine Learning Engineer
Location: Denver CO (Hybrid)
Type: Fulltime
The Machine Learning Engineer is responsible for implementing, deploying, and maintaining machine learning models in a cloud-based ML platform. This role serves as a subject matter expert in Machine Learning Operations (MLOps), bridging the gap between data science and production-grade systems. The engineer will help shape and guide ML solutions within an evolving technology stack and will have the autonomy to recommend and implement best-practice approaches.
RequiredEducation & Experience
- Bachelor’s degree (Master’s preferred) in Statistics, Mathematics, Computer Science, or a related quantitative field.
- 7+ years of experience in data science or a related discipline.
- 3+ years of hands‑on experience with MLOps and production ML systems.
- Proven experience deploying and scaling machine learning models in production environments.
- Strong programming skills in Python and cloud automation/scripting.
- Experience with big data platforms, real‑time/streaming data, and distributed or cluster computing.
- Hands‑on knowledge of cloud platforms, particularly AWS.
- Implement and operationalize data science models in a cloud-based ML platform (e.g., AWS Sage Maker).
- Design and maintain systems to monitor model performance, reliability, and drift in production.
- Act as the MLOps subject matter expert, advising data scientists on model design and deployment considerations.
- Collaborate with data engineering teams to build and maintain data pipelines from enterprise data sources (e.g., Snowflake, time‑series systems).
- Partner with architecture teams to ensure compute, networking, and endpoint requirements are incorporated into ML solutions.
- Stay current with emerging machine learning techniques, tools, and best practices, and apply them where appropriate.
- Work effectively within a geographically distributed team, communicating priorities and project status clearly.
- Design solutions that balance performance, scalability, and cost to meet business objectives.
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