Gen Ai Engineer

Job Summary

We are seeking a highly skilled Generative AI Engineer with 5+ years of software engineering experience and strong expertise in AWS and Azure cloud platforms. The ideal candidate should have hands-on experience designing, developing, deploying, and optimizing LLM-based applications, RAG pipelines, AI agents, and enterprise-grade GenAI solutions. The role requires strong proficiency in Python, cloud-native development, MLOps practices, and modern AI frameworks.

Responsibilities

• Design, develop, and deploy enterprise-scale Generative AI solutions using LLMs and foundation models.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines for knowledge-based applications.
• Develop intelligent AI agents and workflows using LangChain, LangGraph, Semantic Kernel, CrewAI, or similar frameworks.
• Integrate GenAI solutions with enterprise systems, APIs, databases, and third-party services.
• Implement prompt engineering, prompt tuning, and LLM evaluation frameworks.
• Deploy, monitor, and scale AI applications on AWS and Azure cloud environments.
• Design secure and scalable cloud architectures leveraging serverless and containerized services.
• Implement CI/CD pipelines and MLOps best practices for model deployment and lifecycle management.
• Collaborate with Data Scientists, ML Engineers, Product Owners, and Architects to deliver business-focused AI solutions.
• Monitor model performance, latency, cost optimization, hallucination control, and overall system reliability.
• Stay updated on advancements in LLMs, Agentic AI, Multi-Agent Systems, Vector Databases, and Cloud AI services.

Technical Requirements

• GenAI
• Generative AI
• LLM
• RAG
• LangChain
• LangGraph
• Semantic Kernel
• Agentic AI
• AI Agents
• AWS Bedrock
• Azure OpenAI
• Azure AI Studio
• Prompt Engineering
• Vector Database
• Pinecone
• FAISS
• ChromaDB
• FastAPI
• Python
• AKS
• EKS
• Docker
• Kubernetes
• MLOps
• Azure ML
• OpenSearch
• Hugging Face

Additional Responsibilities

• Experience with multi-agent frameworks and autonomous AI workflows.
• Knowledge of Responsible AI, AI Safety, and Governance.
• Familiarity with model monitoring, evaluation, and feedback loops.
• Hands-on experience in productionizing GenAI applications.
• Exposure to distributed systems and microservices architecture.

Educational Requirement

Bachelor of Engineering,Bachelor Of Technology (Integrated)

Preferred Skills

• Technology->AI-Data science->Machine Learning
• Technology->AI-Generative AI->Generative AI for Data Analytics
• Technology->AI-Generative AI->Artificial Intelligence - BASIC

Service Line

Data Analytics Unit