Description

Key Responsibilities

* Design, develop, and deploy *Generative AI applications* using LLMs.

* Build scalable *RAG (Retrieval-Augmented Generation) pipelines* for enterprise use cases.

* Develop *AI agents and agentic workflows* capable of handling multi-step tasks and tool-based interactions.

* Build enterprise *AI copilots* and intelligent automation solutions.

* Use *LangChain / LangGraph* to develop LLM-powered applications and agent workflows.

* Integrate LLMs, APIs, vector databases, and enterprise data sources.

* Implement prompt engineering techniques to improve response quality and reliability.

* Work with embeddings and vector databases for semantic search and knowledge retrieval.

* Optimize GenAI applications for *accuracy, scalability, latency, and cost*.

* Collaborate with product, engineering, and business teams to understand requirements and deliver AI solutions.

* Deploy and maintain GenAI applications in production environments.

 Mandatory Skills

* *Python*

* *Generative AI / LLMs*

* *RAG*

* *AI Agents / Agentic AI*

* *LangChain / LangGraph*

 Good to Have

* Prompt Engineering

* Vector Databases such as Pinecone, FAISS, Chroma, Weaviate, or Milvus

* LLM APIs such as OpenAI, Azure OpenAI, Gemini, Claude, or Llama

* Experience with cloud platforms such as AWS, Azure, or GCP

* Knowledge of LLM evaluation, monitoring, and optimization

* Experience building enterprise AI copilots

 Required Experience

* *4–8 years* of overall software engineering experience.

* Strong hands-on experience in *Python and Generative AI*.

* Proven experience developing *RAG applications and AI agents*.

* Experience working with *LangChain and/or LangGraph*.

* Experience building and deploying production-grade AI solutions is preferred

Key Skills

Education

Bachelor's degree