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AI & machine learning / UTIS-AIML-US-005

AI/ML Engineer (Azure AI / Databricks)

Develop and productionize machine-learning and generative-AI solutions for a global semiconductor company, combining applied modeling with Azure and Databricks MLOps engineering.

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Region
San Diego, California, United States
Work arrangement
Hybrid
Engagement type
Independent contractor with UTIS (1099)
Working hours
Three days onsite per week; must support US East Coast and West Coast business hours.
Posted
September 9, 2026
Applications
Open until filled

What you will work on

  • Frame forecasting, anomaly-detection, classification and optimization problems with engineering and business teams.
  • Build features on Databricks with PySpark, Delta Lake and governed datasets.
  • Develop and evaluate RAG, embeddings, vector search and grounded LLM applications.
  • Build training, retraining and batch or real-time inference pipelines in Azure Machine Learning and Databricks.
  • Use MLflow for reproducible experiments, model registration, versioning and lineage.
  • Implement CI/CD gates and deploy models to managed endpoints or AKS with rollback and monitoring.
  • Monitor data/model drift, latency, quality and cost; document model behavior, risks and operational procedures.

What you bring

  • 6+ years of machine-learning engineering, including enterprise production deployment.
  • Strong Python, pandas, scikit-learn and PyTorch or TensorFlow.
  • Azure Machine Learning, Azure Databricks, Delta Lake, PySpark and SQL.
  • MLflow tracking, registry and deployment.
  • Practical RAG, embeddings, vector stores and evaluation of generative output.
  • Azure OpenAI or comparable managed LLM services, including cost and rate-limit management.
  • CI/CD, Git, Docker and managed endpoint or AKS deployment.
  • Azure identity, security, storage and monitoring fundamentals.
  • Client-facing communication and the ability to work onsite in San Diego three days per week.

Useful additional experience

  • Relevant Microsoft Azure AI/data-science and Databricks ML/data-engineering certifications.
  • Databricks Model Serving, Feature Store, Vector Search or Mosaic AI.
  • Terraform, Azure Bicep, Azure Data Factory or Synapse Analytics.
  • Agent orchestration, fine-tuning or parameter-efficient tuning.
  • Time-series forecasting, predictive maintenance, computer vision and responsible AI practices.

Engagement details

  • Long-term client engagement.
  • Approximate focus: 60% applied ML and model development; 40% MLOps and AI-platform engineering.