AI Engineer -Agentic AI & Context Intelligence - AIML

Role Overview

We are looking for a hands-on AI Engineer to develop context-aware AI solutions, investigate intelligent vehicle architectures, and build memory frameworks for agentic AI systems.

Key Responsibilities

• Context Engineering & Benchmarking: Develop context-aware AI solutions, RAG/GraphRAG pipelines, and evaluation frameworks to measure accuracy, latency, and reliability.
• Intelligent Car Architecture: Investigate AI-driven vehicle architectures, develop PoCs, and implement test scenarios to validate intelligent vehicle functions.
• Agentic AI Memory: Design and implement short-term, long-term, and persistent memory mechanisms for AI agents.
• AI Integration: Build scalable agentic workflows integrating LLMs, APIs, tools, and external systems.
• Testing & Optimization: Develop automated test scenarios, benchmark AI performance, and optimize system efficiency.

Must-Have Technical Skills

• Strong programming experience in Python.
• Minimum of 3 years Hands-on experience with GenAI, LLMs, Agentic AI, and RAG.
• Experience with LangGraph, LangChain, or similar agentic frameworks.
• Knowledge of context engineering, agent memory, and semantic retrieval.
• Experience with vector databases (FAISS, ChromaDB, Weaviate, etc.).
• Experience in AI evaluation, benchmarking, and test automation.
• Understanding of AI system architecture, REST APIs, and MCP.

Good-to-Have Skills

• Knowledge of automotive software architecture and intelligent vehicle systems.
• Experience with GraphRAG, knowledge graphs, and Neo4j.
• Exposure to cloud platforms - AWS Bedrock, Azure AI, or similar platforms.
• Familiarity with automotive simulation and scenario-based testing.

Qualifications

• Bachelor's/Master's degree in Computer Science, AI, or related engineering discipline.
• Strong problem-solving skills with the ability to independently investigate, prototype, and implement AI solutions.