Azure Databricks Machine Learning Engineer

Experience

5+ years in software, data science, or ML engineering, including at least 3 years of hands-on Databricks experience and demonstrated production ML or AI delivery.

Position Summary

Convert data science and AI concepts into secure, scalable, and supportable production solutions. The role combines software engineering, ML engineering, MLOps, AI-first development, and Azure Databricks platform skills.

Key Responsibilities

• Design end-to-end ML pipelines for data preparation, feature engineering, training, evaluation, deployment, inference, monitoring, and retraining.
• Use Databricks, MLflow, Unity Catalog, workflows, model registry, feature engineering, and model serving to deliver governed solutions.
• Build scalable solutions for predictive analytics, optimization, anomaly detection, forecasting, computer vision, NLP, and intelligent automation as business needs require.
• Develop generative AI and agentic AI solutions using grounded enterprise data, tool integration, workflow orchestration, evaluation, observability, guardrails, and human oversight.
• Apply AI-first development practices using coding assistants, automated code and test generation, documentation, and AI-assisted troubleshooting with engineering review.
• Create reusable software components, APIs, batch processes, and deployment patterns for AI and ML applications.
• Implement MLOps and LLMOps practices including tracking, versioning, testing, CI/CD, approval gates, rollback, and environment promotion.
• Monitor model and application quality, drift, reliability, latency, cost, and operational usage.
• Collaborate with data engineering, architecture, data science, applications, cybersecurity, governance, and business teams.
• Document architecture, model behavior, assumptions, limitations, operational procedures, and support requirements.

Required Qualifications

• 5+ years of relevant professional experience, including at least 3 years of hands-on Databricks experience.
• Strong Python and SQL with PySpark, pandas, scikit-learn, MLflow, testing, packaging, and API development.
• Production experience with ML lifecycle management, deployment, monitoring, reproducibility, and retraining or review processes.
• Hands-on experience delivering at least one production ML, generative AI, or agentic AI solution.
• Experience designing agent workflows using enterprise data, tools, APIs, state, guardrails, evaluation, and human approval where appropriate.
• Azure experience with ADLS Gen2, Key Vault, Microsoft Entra ID, monitoring, Azure AI services or Azure Machine Learning, and secure deployment patterns.
• Experience with Git, automated testing, CI/CD, software engineering standards, and production support.

Preferred Qualifications

• Experience in manufacturing, industrial, supply-chain, or regulated environments.
• Experience with time-series, optimization, computer vision, NLP, RAG, knowledge graphs, or intelligent automation.
• Familiarity with responsible AI, model risk, privacy, security, evaluation, and human-in-the-loop design.

Certification Requirements

• Preferred: Databricks Certified Machine Learning Associate or Databricks Certified Machine Learning Professional
• Preferred: Microsoft Certified: Azure AI Engineer Associate (AI-102).
• Preferred: Microsoft Certified: Azure Data Scientist Associate (DP-100)
• Preferred for GenAI-focused work: Databricks Certified Generative AI Engineer Associate.