Sr . Generative AI Engineer

Location: Gota, Ahmedabad (Work from Office)

Experience: 23 Years

Employment Type: Full-Time

Preferred Qualifications

• Bachelors or Masters degree in Computer Science, AI, Data Science, or a related field.

• Experience building and deploying production-grade Generative AI applications.

• Familiarity with MCP (Model Context Protocol), AI evaluation frameworks, observability tools, and MLOps.

• Contributions to open-source projects, AI research, or technical publications are a plus.

Role Objective

Design, develop, and optimize cutting-edge Generative AI solutions that solve complex business challenges while delivering scalable, secure, and production-ready AI applications.

Key Responsibilities

• Design, develop, and deploy Generative AI and LLM-based applications.

• Build and optimize Retrieval-Augmented Generation (RAG) pipelines.

• Develop AI agents and multi-agent workflows using modern AI frameworks.

• Integrate LLMs with enterprise applications, APIs, databases, and external tools.

• Fine-tune prompts and optimize model performance, latency, cost, and accuracy.

• Develop scalable backend services using Python, FastAPI, or Flask.

• Implement vector search solutions using modern vector databases.

• Deploy AI applications using Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).

• Collaborate with Product, Engineering, and Business teams to deliver AI-driven solutions.

• Mentor junior engineers through code reviews, technical guidance, and best practices.

• Evaluate and adopt emerging AI technologies, frameworks, and research.

• Ensure AI solutions follow security, scalability, and performance best practices.

Key Skills

• Programming: Advanced Python programming and API development.

• Generative AI: Strong knowledge of LLMs, Prompt Engineering, RAG, AI Agents, and Agentic AI.

• AI Frameworks: Experience with LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or similar frameworks.

• LLMs: Hands-on experience with OpenAI, Gemini, Claude, Llama, Mistral, or equivalent foundation models.

• Databases: SQL, NoSQL, and Vector Databases (Pinecone, Qdrant, ChromaDB, Weaviate, Milvus).

• Backend Development: FastAPI, Flask, REST APIs, and microservices.

• Cloud DevOps: Docker, Kubernetes, Git, CI/CD, AWS, Azure, or GCP.

• System Design: Ability to design scalable, production-ready AI architectures.

• Performance Optimization: Experience optimizing AI applications for latency, cost, scalability, and reliability.

• Leadership: Ability to mentor junior engineers and drive technical excellence.

• Communication: Excellent verbal and written English communication skills with the ability to explain technical concepts to clients and stakeholders.

• Problem Solving: Strong analytical thinking, debugging, and decision-making skills.

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