About the Role:-
We are looking for a Generative AI Engineer to design, build, and deploy LLM-powered applications and autonomous agentic AI systems. You will work closely with product, data, and engineering teams to turn foundation models into reliable, scalable, production-grade solutions.
Key Responsibilities:-
- Design and build applications on top of Large Language Models (open-source and proprietary, such as GPT, Claude, Llama, Mistral, and Gemini).
- Develop agentic AI workflows where LLMs plan, reason, call tools, and execute multi-step tasks using frameworks such as LangChain, LangGraph, LlamaIndex, or CrewAI.
- Implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embedding generation, vector search, and re-ranking.
- Write clean, modular, and well-tested Python code for data processing, API services, and model integration.
- Build and expose APIs (FastAPI or Flask) for LLM-based services and integrate them with enterprise systems.
- Fine-tune or adapt models using techniques such as LoRA/QLoRA and instruction tuning when task requirements justify it.
- Design prompts, evaluation harnesses, and guardrails to improve output quality, safety, and reliability.
- Monitor model performance, latency, cost, and hallucination rates in production; iterate based on metrics.
- Collaborate with product managers, data scientists, and DevOps engineers to define requirements and ship features.
- Stay current with advances in LLMs, agent architectures, and GenAI tooling, and share learnings with the team.
Required Skills and Qualifications:-
- Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.
- 3+ years of software development experience, with strong proficiency in
Python
(OOP, async programming, packaging, testing with pytest). - Hands-on experience building applications with
LLMs
via APIs (OpenAI, Anthropic, Azure OpenAI, Hugging Face, or similar). - Practical experience with
Agentic AI
concepts: tool/function calling, ReAct-style reasoning, multi-agent orchestration, and memory management. - Working knowledge of RAG architecture and vector databases (FAISS, Pinecone, Weaviate, Chroma, or pgvector).
- Experience with prompt engineering and evaluation of LLM outputs (e.g., RAGAS, DeepEval, or custom metrics).
- Familiarity with REST API development, Docker, and cloud platforms (AWS, Azure, or GCP).
- Solid understanding of NLP fundamentals, embeddings, and transformer architectures.
- Strong problem-solving skills and the ability to communicate technical trade-offs clearly.
Interested candidates kindly share your updated resume to vijayravi.r@prodapt.com with the below details.
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