Agentic AI

About Client :

Our client is a global digital solutions and technology consulting company headquartered in Mumbai, India. The company generates annual revenue of over $4.29 billion (₹35,517 crore), reflecting a 4.4% year-over-year growth in USD terms. It has a workforce of around 86,000 professionals operating in more than 40 countries and serves a global client base of over 700 organizations.

Our client operates across several major industry sectors, including Banking, Financial Services & Insurance (BFSI), Technology, Media & Telecommunications (TMT), Healthcare & Life Sciences, and Manufacturing & Consumer. In the past year, the company achieved a net profit of $553.4 million (₹4,584.6 crore), marking a 1.4% increase from the previous year. It also recorded a strong order inflow of $5.6 billion, up 15.7% year-over-year, highlighting growing demand across its service lines.

Key focus areas include Digital Transformation, Enterprise AI, Data & Analytics, and Product Engineering—reflecting its strategic commitment to driving innovation and value for clients across industries.

Job Description : Location : India

Experience : 5+

WORK MODE : Hybrid

Job Description

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.