GenAI Engineer

Interview Mode : Virtual

Notice Period : Immediate Joiners or Serving Notice (Maximum 710 Days)

Preference : Local to NCR Only

Previous MNC Experience : Mandatory

About the Role :

Looking for a skilled GenAI Engineer with hands-on experience in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Python, and Generative AI application development. The ideal candidate should have experience designing, developing, and deploying AI-powered solutions using modern GenAI frameworks while collaborating with cross-functional teams to build scalable and production-ready AI applications.

Key Responsibilities :

- Design, develop, and deploy Generative AI applications using Large Language Models (LLMs).

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise AI use cases.

- Develop scalable backend services using Python.

- Integrate LLMs with enterprise applications through APIs and microservices.

- Implement prompt engineering techniques to improve model accuracy and response quality.

- Build document ingestion, embedding, indexing, and semantic search solutions.

- Develop AI assistants, chatbots, and knowledge retrieval systems.

- Optimize LLM performance, latency, token usage, and inference costs.

- Work with vector databases for semantic search and contextual retrieval.

- Integrate AI applications with cloud services and external APIs.

- Collaborate with product, engineering, and business teams to deliver AI-driven solutions.

- Troubleshoot, debug, and continuously improve GenAI applications.

- Ensure AI solutions follow security, scalability, and best development practices.

Requirements :

- 4+ years of software development experience with strong expertise in Python.

- Minimum 1+ year of hands-on experience in Generative AI / LLM-based application development.

- Strong experience working with Retrieval-Augmented Generation (RAG) architecture.

- Hands-on experience with Large Language Models (OpenAI GPT, Azure OpenAI, Claude, Llama, Gemini, Mistral, or similar).

- Strong knowledge of Prompt Engineering and prompt optimization techniques.

- Experience with LangChain, LlamaIndex, CrewAI, or similar GenAI frameworks.

- Experience implementing embeddings, vector search, and semantic retrieval.

- Hands-on experience with vector databases such as Pinecone, ChromaDB, FAISS, Weaviate, Milvus, or Qdrant.

- Strong understanding of REST APIs, API integrations, and microservices.

- Experience integrating AI models with enterprise applications.

- Strong knowledge of JSON, API authentication, and data processing.

- Experience with Git, version control, and Agile development methodologies.

- Good understanding of Docker and containerized application deployment.

- Exposure to AWS, Azure, or Google Cloud AI services.

- Strong debugging, analytical, and problem-solving skills.

- Excellent verbal and written communication skills.