• Design and build AI/ML data pipelines for model metadata, data lineage, and performance tracking
• Build scalable data infrastructure using Databricks & Apache Spark
• Develop MCP servers and enable AI data distribution through MCP
• Build feature engineering and data preprocessing pipelines
• Implement MLflow for experiment tracking, model versioning, and model registry
• Develop AI agent discovery, classification, and inventory workflows
• Design knowledge graphs for AI model relationships, dependencies, and data lineage
• Build real-time pipelines for model monitoring, drift detection, and performance tracking
• Implement AI-specific data quality, governance, security, and compliance frameworks
• Collaborate closely with Data Scientists, ML Engineers, and Architects

? 10+ years of overall experience

? 5+ years of hands-on Data Engineering experience

? 2+ years working with AI/ML workloads

? Strong Python expertise

? Databricks, Apache Spark & distributed computing

? Strong understanding of ML lifecycle: training, deployment & monitoring

? Feature engineering and ML data pipelines

? MLflow / Model Registry / Experiment Tracking

? Strong SQL skills

? Experience with vector databases, embeddings & similarity search

? Understanding of data/model governance and AI security

? Nice to Have

• Generative AI / RAG / Fine-tuning
• LangChain / HuggingFace
• MCP experience
• Azure ML / AWS SageMaker / Google Vertex AI
• Neo4j / Amazon Neptune / Knowledge Graphs
• AI explainability & interpretability
• ML A/B testing frameworks
• Databricks / AWS / Azure / GCP certifications
• Open-source ML contributions or publications

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