Key Responsibilities
- Design and develop LLMpowered agents and intelligent workflows to solve complex business problems.
- Build and optimize RetrievalAugmented Generation (RAG) pipelines using embeddings and vector databases.
- Engineer highquality prompts, including fewshot learning, structured outputs, and tool/function calling.
- Implement texttoSQL and automated insight generation pipelines for analytics use cases.
- Design and maintain evaluation frameworks to measure model accuracy, latency, reliability, and cost.
- Collaborate closely with backend and platform teams to productionize, scale, and monitor AI systems.
- Continuously experiment, debug, and improve model behavior through structured testing and iteration.
Required Skills
- Strong handson experience in Python.
- Practical experience working with LLM APIs (e.g., OpenAI, Anthropic).
- Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or similar.
- Solid understanding of embeddings, semantic search, vector databases, and RAG architectures.
- Strong debugging, experimentation, and problemsolving mindset in applied AI systems.
Preferred / GoodtoHave Skills
- Experience with MLOps / LLMOps tooling such as MLflow, Langfuse, Weights & Biases (W&B).
- Exposure to multiagent systems and agent coordination patterns.
- Domain experience in analytics, data platforms, or pharma/life sciences.
- Knowledge of cost optimization techniques, model selection, and routing strategies across multiple LLMs.
Notice Period : Immediate to 30 Days
Location : PAN India
