ML Ops Engineer

ML Ops Engineer responsible for designing, building, automating, deploying, monitoring, and supporting machine learning solutions across the enterprise, bridging Data Science, Data Engineering, DevOps, Cloud Engineering, and Architecture teams. The role focuses on establishing and supporting enterprise ML Ops capabilities, including CI/CD pipelines, model deployment frameworks, feature management, model monitoring, governance controls, and automated retraining processes.

What you'd actually do

  1. Design and implement enterprise ML Ops platforms and frameworks.
  2. Build reusable ML deployment patterns and automation.
  3. Develop model serving and inference architectures.
  4. Build and maintain CI/CD pipelines for machine learning workloads.
  5. Convert experimental data science solutions into production-grade systems.

Skills

Required

  • 5–8 years of IT experience
  • 3+ years in ML Ops, ML Engineering, Data Engineering, or Platform Engineering
  • Experience in Azure ML, Domino, Azure Databricks, ADF
  • Good understanding of supervised learning, unsupervised learning, model evaluation, feature engineering, model life cycle management
  • Experience deploying machine learning solutions into production
  • Experience with cloud-native data and AI platforms
  • Experience implementing CI/CD pipelines
  • Strong analytical skills
  • Excellent verbal and written communication skills

Nice to have

  • Financial Services, Insurance, or highly regulated industry experience
  • Experience supporting enterprise-scale AI platforms
  • Exposure to GenAI, LLMOps, and Agentic AI frameworks

What the JD emphasized

  • deploying machine learning solutions into production
  • Experience in Azure ML, Domino, Azure Databricks, ADF
  • Experience with cloud-native data and AI platforms.
  • Experience implementing CI/CD pipelines.
  • Financial Services, Insurance, or highly regulated industry experience.

Other signals

  • ML Ops Engineer
  • deploying machine learning solutions into production
  • CI/CD pipelines for machine learning workloads