Gen Ai Engineer

Job description

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