Agentic AI Engineer / Lead

Job Description :
We are looking for an experienced AI Engineer / Lead with hands-on expertise in Generative AI and Agentic AI to design, develop, and deploy production-grade, enterprise-scale AI solutions . The ideal candidate should have strong proficiency in Python, Machine Learning , and multi-agent system orchestration , with proven experience delivering end-to-end implementations with minimal supervision.
Key Responsibilities
Design, build, and orchestrate multi-agent systems capable of autonomous decision-making and task execution.
Lead the development and deployment of Generative AI solutions using LLMs and fine-tuned models.
Implement RAG (Retrieval-Augmented Generation) pipelines with robust document parsing, re-ranking, and context optimization.
Integrate AI agents into enterprise systems through APIs, function calling, and workflow orchestration frameworks (e.g., LangGraph, CrewAI, LlamaIndex, Haystack).
Fine-tune and evaluate LLMs (using LoRA, PEFT, or QLoRA) for domain-specific use cases.
Collaborate cross-functionally with data, platform, and DevOps teams to ensure scalable and secure AI deployments.
Ensure production-grade quality performance optimization, monitoring, and continuous improvement.
Provide technical mentorship to junior engineers (minimal team handling required).
Required Skills & Experience
5 -8 years of total experience, with Min 3+ years in Generative AI and 1+ years Agentic AI with Min 2 or 3 Production grade implementation at Enterprise Level.
Strong background in Machine Learning, Deep Learning , and Python programming .
Hands-on experience with LLM frameworks (LangChain, LlamaIndex, Haystack, Semantic Kernel, etc.).
Proficiency in multi-agent orchestration (CrewAI, LangGraph, Swarm, Autogen, or custom frameworks).
Expertise in vector databases (FAISS, Pinecone, Chroma, Weaviate, etc.) and embedding models .
Proven fine-tuning experience using LoRA, QLoRA, or PEFT.
Experience in enterprise-grade GenAI implementations — from PoC to production.
Strong understanding of RAG architecture , document chunking , context optimization , and model evaluation .
Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Excellent problem-solving and debugging skills.