Senior MLOps Engineer: Production AI & LLM Systems

dynatronsoftware; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Remote work environment

Professional development opportunities

Ownership of production systems

Cross-team collaboration

Job summary

Dynatron seeks a Senior Machine Learning Engineer (MLOps) to own the production infrastructure and operational lifecycle behind its AI and machine learning capabilities. You’ll design and operate pipelines, governance, and deployment practices enabling our Data Science team to move models from development to production safely and repeatedly.

You’ll support traditional ML and generative AI workloads, ensuring reliability, observability, security, and cost efficiency in production.

Qualifications

• 6+ years in software/ML engineering or related field.
• 3+ years deploying and operating AI/ML systems in production.
• Experience with traditional ML and LLM workloads in production.
• Understanding of model lifecycle from development to retirement.

Responsibilities

• Design, build, and maintain deployment pipelines from development to production.
• Establish model versioning, lineage, registry, and automated promotion.
• Define production-readiness standards and deployment patterns for ML/AI workloads.
• Partner with Data Scientists to enable efficient, production-ready handoffs.
• Own production monitoring for performance, drift, data quality, latency, and availability.
• Deploy and support production LLM applications including retrieval-based and agentic architectures.
• Build training and retraining infrastructure and automated pipelines.
• Establish AI/ML governance, access controls, and documentation.

Skills

MLOps experience

Production ML

LLM workloads

Python engineering

Cloud platforms

Education

Bachelor's degree in CS/Engineering

Tools

Model registries

Pipeline orchestration

ML CI/CD

Containerization

IaC

Job description

Dynatron seeks a Senior Machine Learning Engineer (MLOps) to own the production infrastructure and operational lifecycle behind its AI and machine learning capabilities. You’ll design and operate pipelines, governance, and deployment practices enabling our Data Science team to move models from development to production safely and repeatedly.

You’ll support traditional ML and generative AI workloads, ensuring reliability, observability, security, and cost efficiency in production.