Data & AI Engineer - Customer Analytics AIML

Role Overview

We are looking for a hands-on Data & AI Engineer with strong expertise in Generative AI and Agentic AI to design, develop, and deploy intelligent AI applications. The role focuses on building autonomous AI agents, context-aware systems, advanced retrieval solutions, and scalable AI architectures, supported by data engineering capabilities.

Key Responsibilities

• GenAI Development: Design and develop LLM-powered applications using RAG, prompt engineering, and advanced retrieval techniques.
• Agentic AI: Build autonomous and multi-agent systems capable of reasoning, planning, tool execution, and workflow orchestration.
• Context Engineering: Develop intelligent context retrieval, semantic search, and GraphRAG solutions.
• Agent Memory: Implement short-term, long-term, and persistent memory for context-aware AI agents.
• AI Architecture: Design scalable AI services, APIs, and integrations with enterprise systems.
• AI Evaluation: Develop benchmarking, automated testing, and observability frameworks to evaluate agent performance and reliability.
• Data Integration: Build lightweight data pipelines to ingest, transform, and prepare structured and unstructured data for AI applications.

Must-Have Technical Skills

• Strong programming experience in Python and API development.
• Hands-on experience with LLMs, Generative AI, RAG, and Agentic AI.
• Experience with LangGraph, LangChain, AutoGen, or similar agent frameworks.
• Knowledge of agent orchestration, tool calling, MCP, and multi-agent workflows.
• Experience with context engineering, embeddings, semantic search, and vector databases.
• Understanding of agent memory architectures and knowledge retrieval.
• Experience with AI evaluation, benchmarking, and observability.
• Working knowledge of SQL, data integration, and cloud AI platforms (AWS/Azure).

Good-to-Have Skills

• Experience with GraphRAG, knowledge graphs, and Neo4j.
• Knowledge of AWS Bedrock, Azure AI Foundry, or similar platforms.
• Experience with Docker, Kubernetes, CI/CD, and LLMOps.
• Familiarity with data pipelines, Databricks, or Apache Spark.
• Exposure to automotive AI, connected vehicles, or intelligent car systems.

Qualifications

• Bachelor's/Master's degree in Computer Science, AI, Data Science, or related engineering discipline.
• Strong problem-solving skills and experience building production-ready AI solutions.
• Ability to independently design, prototype, and implement AI applications.