Data Engineer

About Client: Our Client is a multinational IT services and consulting company headquartered in USA, With revenues 19.7 Billion USD, with Global work force of 3,50,000 and Listed in NASDAQ, It is one of the leading IT services firms globally, known for its work in digital transformation, technology consulting, and business process outsourcing, Business Focus on Digital Engineering, Cloud Services, AI and Data Analytics, Enterprise Applications ( SAP, Oracle, Sales Force ), IT Infrastructure, Business Process Out Source. Major delivery centers in India, including cities like Chennai, Pune, Hyderabad, and Bengaluru. Offices in over 35 countries. India is a major operational hub, with as its U.S. headquarters. Job Title : Data Engineer Key Skills : Snowflake, Data Engineering, Agentic AI Job Locations : Pune, Bangalore, Hyderabad, Chennai. Experience : 5+ Years. Education Qualification : Any Graduation. Work Mode : Hybrid. Employment Type : Contract. Notice Period : Immediate to 15 Days Key Responsibilities: Design and develop knowledge bases tailored for Agentic AI solutions. Implement optimal ingestion mechanisms aligned with semantic search and Retrieval-Augmented Generation (RAG) goals using vector databases. Apply prompt engineering techniques and utilize prompt libraries to enhance agent performance. Orchestrate multi-agent systems using MCP protocol and related frameworks. Collaborate with cross-functional teams to integrate ML Ops best practices including feature stores, feedback loops, and continuous model improvement. Work closely with stakeholders to align AI capabilities with business use cases and outcomes. Required Skills & Experience: Overall Experience (5+ years) Proven experience in building knowledge bases for Agentic AI systems. Strong understanding of vector databases (e.g., FAISS, Pinecone, Weaviate) and semantic search strategies. Expertise in prompt engineering and familiarity with prompt libraries (e.g., LangChain, Guidance). Hands-on experience with MCP protocol and agent orchestration frameworks. Solid background in ML Ops including feature store management, feedback loop design, and deployment pipelines. Familiarity with Snowflake and a preference for leveraging Cortex AI modules. Flexibility in technology stack with a strong foundation in Agentic AI principles and implementation.