Own the end-to-end operationalization of ML and AI solutions, ensuring models move smoothly from development to scalable, reliable production products
Job Summary
Own the end-to-end operationalization of ML and AI solutions, ensuring models move smoothly from development to scalable, reliable production products.
Design, automate, and maintain CI/CD pipelines for model training, testing, deployment, and retraining using tools like Azure DevOps and Databricks.
Collaborate closely with Data Scientists, Data Engineers, and Data Architects to transform high-quality research models into robust, production-grade products.
Matching Summary
Own the end-to-end operationalization of ML and AI solutions, ensuring models move smoothly from development to scalable, reliable production products.
Skills & Requirements
Must-have
operationalization of ML/AI solutions
design and automate CI/CD pipelines
model lifecycle workflows
monitor deployed models
Python, PySpark, and SQL
feature engineering fundamentals
Vector embeddings & RAG systems
MLflow for model tracking
CI/CD pipelines (Azure DevOps preferred)
data versioning, model versioning
designing, consuming, or integrating REST APIs
Nice-to-have
systems-thinking mindset
contribute to ML/AI platform evolution
infrastructure-as-code
Unix environments and DevOps principles
real-time or near-real-time serving architectures
AI agent tools and MCP servers
Key Requirements
7+ years of experience
production-ready code
practical understanding of data engineering fundamentals