Senior Data & AI Engineer

Senior Data & AI Engineer – Data & AI Platform

We are looking for a Senior Data & AI Engineer to design, build, and operationalize scalable AI/ML solutions within our Data and AI Platform. This role focuses on enabling enterprise-wide AI adoption by developing robust data pipelines, deploying ML models, and building reusable AI services aligned with platform standards.

You will work at the intersection of Data Engineering, Machine Learning, and Platform Architecture, ensuring AI solutions are production-ready, governed, observable, secure, and cost-efficient.

Key Responsibilities

1. AI/ML Solution Development

• Design and implement scalable Machine Learning and Generative AI solutions on Databricks and Azure/AWS
• Build and optimize feature engineering pipelines and training workflows
• Develop reusable AI components, APIs, and enterprise AI services

2. Platform & MLOps Enablement

• Build and maintain end-to-end ML pipelines including training, validation, deployment, and monitoring
• Implement CI/CD pipelines for ML models and AI applications
• Integrate AI workloads with tools such as:
  • Databricks
  • MLflow
  • Unity Catalog

3. Data Engineering & Integration

• Collaborate with Data Engineers to ensure high-quality and governed datasets
• Work with both structured and unstructured data across batch and streaming pipelines
• Optimize data pipelines for performance, scalability, and cost efficiency

4. AI Governance & Observability

• Implement model monitoring, drift detection, and performance tracking
• Ensure compliance with AI governance, security, and regulatory standards
• Enable auditability, lineage tracking, and observability for AI models

Required Skills & Qualifications

Advanced Data Engineering – Databricks Ecosystem

Databricks Platform Expertise

Strong hands-on experience with the Databricks ecosystem, including:

• Delta Lake
• Delta Live Tables
• Unity Catalog
• Databricks Workflows
• Serverless Compute
• Mosaic AI
• Model Serving Endpoints

Data Pipeline Engineering

Build scalable batch and streaming pipelines using:

• Apache Spark
• Structured Streaming
• Apache Kafka

Additional expertise required in:

• Medallion Architecture (Bronze / Silver / Gold)
• CDC implementation and incremental processing strategies
• Schema evolution and data contract enforcement

Performance Optimization

• Query tuning and execution plan optimization
• Partitioning, clustering, and Z-ordering
• Cost optimization for Databricks workloads
• Autoscaling and workload sizing strategies

Foundation Models & Generative AI Engineering

Foundation Model Experience

Hands-on experience with enterprise usage of:

• OpenAI models
• Anthropic models
• Meta Llama family
• Google Gemini
• Mistral AI models
• Open-source deployment patterns

Model Engineering

• Fine-tuning and instruction tuning
• LoRA / QLoRA optimization
• Quantization strategies
• Model serving and inference optimization

LLMOps

• Prompt lifecycle management
• Evaluation pipelines for LLM outputs
• Hallucination mitigation
• Guardrails and output validation

Retrieval-Augmented Generation (RAG) & Knowledge Systems

RAG Architecture

• Design and implementation of production-grade RAG systems
• Hybrid retrieval strategies
• Context window optimization
• Chunking and embedding strategies

Vector Databases

Hands-on experience with:

• FAISS
• Pinecone
• Milvus
• Databricks Vector Search

Embedding Systems

• Embedding model selection and benchmarking
• Semantic search optimization
• Knowledge indexing pipelines

AI Agent & Multi-Agent Systems

Agentic AI Development

• Design of autonomous and semi-autonomous AI agents
• Tool-calling orchestration
• Workflow-based reasoning systems
• Memory management strategies

Multi-Agent Systems (MAS)

• Agent coordination patterns
• Task decomposition frameworks
• Agent collaboration protocols
• Conflict resolution strategies

Experience with:

• LangChain
• LangGraph
• AutoGen

AI Communication Protocols & Integration Standards

Model Context Protocol (MCP)

• Building and deploying MCP-compliant servers
• Tool exposure and orchestration
• Secure model-tool interaction design

Agent-to-Agent (A2A) Protocols

• Understanding inter-agent communication patterns
• Event-driven agent coordination
• Distributed AI workflow orchestration

API & Service Mesh Integration

• REST / gRPC service integration
• Event-driven AI services
• Enterprise service bus integration

Modern AI Platform Engineering

Serving Infrastructure

• Real-time inference serving
• Batch inference pipelines
• Autoscaling inference endpoints

Containerization & Orchestration

• Docker
• Kubernetes
• Helm deployment patterns

Emerging AI Platform Standards

• Experience with MCP server development
• Exposure to A2A communication protocols
• AI agent orchestration platforms
• Enterprise-grade tool-use governance

Databricks AI Platform

Experience with:

• Mosaic AI Agent Framework
• Databricks Vector Search
• AI Gateway
• Lakehouse Monitoring

Nice-to-Have Use Case Exposure

• AI copilots for data engineering workflows
• Autonomous data quality remediation
• Metadata-aware AI systems
• Semantic data discovery
• Agentic ETL orchestration
• Natural language-to-SQL systems
• Enterprise knowledge assistants
• LLM-powered observability platforms