Job role: Generative AI Engineer + AWS

Location: Noida, Bangalore, Gurugram, Pune

Experience : 3+ Years 

Job Summary:

We are looking for a highly capable and innovative in Generative AI to join our Data Science COE Team. You will lead the development and deployment of GenAI solutions, including LLM-based applications, prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG) for enterprise use cases.

The ideal candidate has a strong foundation in machine learning and NLP, with hands-on experience in modern GenAI tools and frameworks such as OpenAI, LangChain, Langgraph, A2A, Strands, Hugging Face, Bedrock, Agentcore or similar.

Key Responsibilities:

· Design and build Generative AI solutions using Large Language Models (LLMs) for business problems across domains like customer service, document automation, summarization, and knowledge retrieval.

· Fine-tune or adapt foundation models using domain-specific data.

· Implement RAG pipelines, embedding models, vector databases (e.g., FAISS, Pinecone, ChromaDB).

· Collaborate with data engineers, MLOps, and product teams to build end-to-end AI applications and APIs for POC or Products for Clients.

· Develop custom prompts and prompt chains using tools like LangChain, LlamaIndex, or custom frameworks.

· Evaluate model performance, mitigate bias, and optimize accuracy, latency, and cost.

· Stay up to date with the latest trends in LLMs, transformers, and GenAI architecture.

Required Skills:

• Generative AI & Agentic AI – hands-on experience building and deploying production-grade GenAI/Agentic AI solutions

• AWS AI/Cloud – strong experience with AWS Bedrock, AWS Lambda, and AWS deployment

• LangChain & LangGraph – experience developing LLM workflows, agents, and multi-agent applications

• Multimodal AI – experience with multimodal models, computer vision models, and LLM-based applications

• Production Deployment – proven experience taking AI solutions from development to production, including deployment, integration, monitoring, and scalability

• AI/LLM Engineering – experience building real-world LLM applications, not limited to POCs or prototypes

Good to Have

• Microsoft Azure experience

Job role: Generative AI Engineer + AWS

Location: Noida, Bangalore, Gurugram, Pune

Experience : 3+ Years 

Job Summary:

We are looking for a highly capable and innovative in Generative AI to join our Data Science COE Team. You will lead the development and deployment of GenAI solutions, including LLM-based applications, prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG) for enterprise use cases.

The ideal candidate has a strong foundation in machine learning and NLP, with hands-on experience in modern GenAI tools and frameworks such as OpenAI, LangChain, Langgraph, A2A, Strands, Hugging Face, Bedrock, Agentcore or similar.

Key Responsibilities:

· Design and build Generative AI solutions using Large Language Models (LLMs) for business problems across domains like customer service, document automation, summarization, and knowledge retrieval.

· Fine-tune or adapt foundation models using domain-specific data.

· Implement RAG pipelines, embedding models, vector databases (e.g., FAISS, Pinecone, ChromaDB).

· Collaborate with data engineers, MLOps, and product teams to build end-to-end AI applications and APIs for POC or Products for Clients.

· Develop custom prompts and prompt chains using tools like LangChain, LlamaIndex, or custom frameworks.

· Evaluate model performance, mitigate bias, and optimize accuracy, latency, and cost.

· Stay up to date with the latest trends in LLMs, transformers, and GenAI architecture.

Required Skills:

• Generative AI & Agentic AI – hands-on experience building and deploying production-grade GenAI/Agentic AI solutions

• AWS AI/Cloud – strong experience with AWS Bedrock, AWS Lambda, and AWS deployment

• LangChain & LangGraph – experience developing LLM workflows, agents, and multi-agent applications

• Multimodal AI – experience with multimodal models, computer vision models, and LLM-based applications

• Production Deployment – proven experience taking AI solutions from development to production, including deployment, integration, monitoring, and scalability

• AI/LLM Engineering – experience building real-world LLM applications, not limited to POCs or prototypes

Good to Have

• Microsoft Azure experience