5+ years of hands-on Machine Learning Engineering, MLOps, or AI Engineering experience within enterprise production environments. Extensive experience designing and deploying Databricks Lakehouse solutions using Delta Lake, Unity Catalog, MLflow, Databricks SQL, Workflows, and Delta Live Tables. Strong programming expertise in Python, PySpark, Pandas, NumPy, and modern software engineering best practices. Experience building, training, deploying, and monitoring machine learning models using PyTorch, TensorFlow, scikit-learn, XGBoost, or similar ML frameworks. Proven experience implementing end-to-end MLOps pipelines including experiment tracking, model registry, automated retraining, model deployment, and production monitoring. Hands-on experience developing Generative AI (GenAI) and Large Language Model (LLM) solutions, including RAG architectures, prompt engineering, LangChain, LlamaIndex, or Databricks Mosaic AI. Experience implementing Vector Search, embeddings, semantic search, and AI retrieval pipelines using Databricks or similar vector database technologies. Strong understanding of Apache Spark internals, distributed computing, performance tuning, partitioning, memory management, and large-scale data processing. Experience with cloud platforms including AWS, Azure, or Google Cloud Platform, supporting enterprise AI and machine learning workloads. Experience implementing CI/CD pipelines, GitHub Actions, Azure DevOps, Databricks Asset Bundles, and modern DevOps automation practices. Hands-on experience with Docker, Kubernetes, Terraform, or Pulumi supporting Infrastructure-as-Code and scalable AI platform deployments. Experience working with Delta Lake optimization, Unity Catalog governance, data lineage, access controls, and enterprise data governance practices. Strong experience monitoring model performance, model drift, data quality, observability, and production SLAs using tools such as Prometheus, Grafana, or Databricks Lakehouse Monitoring. Demonstrated ability to collaborate with Data Scientists and Data Engineers to productionize AI and machine learning solutions while conducting code reviews, architecture reviews, and technical mentoring. Experience supporting modern AI ecosystems including Feature Store, Databricks Model Serving, Kafka, Spark Structured Streaming, Lakehouse Architecture, and responsible AI or ML governance practices.

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