As an AI Engineer, you will design and build next-generation AI applications
that leverage agentic workflows, large language models, and enterprise knowledge
systems. You will work across the full software lifecycle, from architecture and
backend development to deployment, evaluation, monitoring, and optimization.
The ideal candidate is excited about the potential of AI while maintaining
robust engineering judgment, carefully balancing reliability, security,
performance, operational complexity, cost, and business value.

CORE TECHNICAL AREAS

Agent Architectures & Multi-Agent Systems
Agent orchestration frameworks such as Google ADK, LangChain, LangGraph,
DeepAgents, AutoGen, CrewAI, or similar
Planning and reasoning workflows
Tool integration and function calling
State and memory management
Human-in-the-loop review processes
Error handling and recovery mechanisms

LLMs & Generative AI
Transformer fundamentals and attention mechanisms
Prompt engineering and structured outputs
Context management and token optimization
Fine-tuning and model customization




Working with both commercial and open-source models

RAG & Knowledge Systems
Semantic and hybrid search
Document ingestion and processing pipelines
Embeddings and retrieval strategies
Reranking techniques
Vector databases such as Pinecone, Qdrant, Weaviate, pgvector, or Vertex AI
Vector Search
Knowledge access controls and source citation capabilities

Backend & Cloud Engineering
Python, FastAPI, and asynchronous programming
REST and streaming APIs
Event-driven and microservice architectures
Docker and cloud deployment
Security, monitoring, and observability

AI-Augmented Development
Development acceleration using tools such as Cursor, GitHub Copilot, Claude
Code, or similar
Frontend development using React, Next.js, and TypeScript

Evaluation & Reliability
Agent evaluation and testing
Observability and tracing
Prompt security and guardrails
Latency, quality, and

📌 Ai Engineer Chennai (India)
🏢 Ford Motor
📍 India