Agentic AI

4 weeks ago

Thane, India People Prime Worldwide Full time

About Company : Our Client is a leading Indian multinational IT services and consulting firm. It provides digital transformation, cloud computing, data analytics, enterprise application integration, infrastructure management, and application development services. The company caters to over 700 clients across industries such as banking and financial services, manufacturing, technology, media, retail, and travel & hospitality.

Its industry-specific solutions are designed to address complex business challenges by combining domain expertise with deep technical capabilities. With a global workforce of over 80,000 professionals and a presence in more than 50 countries.


Job Title: Agentic AI

Locations: PAN INDIA

Experience: 10+ Years (Relevant)

Employment Type: Contract to Hire

Work Mode : Work From Office

Notice Period : Immediate to 15 Days

Job Description:


Agentic AI, Azure Architecture, ADF, Structured and Unstructured Fiel processing, LLM, Python


Architectural Leadership: Own the end-to-end architecture and technical design for the agentic chatbot and its supporting systems within Azure. This includes all components of the retrieval-augmented generation (RAG) pipeline, multi-agent orchestration, and integration layers.

Solution Design: Create detailed architectural blueprints for a system capable of handling over 200,000 enterprise documents. This involves defining the data flow, selecting appropriate Azure services, and designing for scalability, performance, and cost-efficiency.

Data Strategy: Formulate the strategy for ingesting, processing, and indexing large-scale document repositories using ADF and Azure Functions. This includes designing embedding pipelines, vector database solutions (e.g., Azure Cognitive Search), and knowledge graph integration.

Agentic AI Implementation: Lead the design and implementation of multi-agent systems using frameworks like LangChain, LangGraph. Develop agents that can perform multi-step operations, use tools, and communicate with other agents to fulfill complex user requests.

LLM Integration: Evaluate and select appropriate LLMs, including both hosted (Azure OpenAI) and open-source models (e.g., LLAMA), based on performance, cost, and specific task requirements. Oversee prompt engineering strategies and model fine-tuning where necessary.

Azure Stack Expertise: Leverage deep knowledge of the Azure AI stack, including Azure AI Search, Azure Machine Learning, Azure OpenAI Service, Azure ADF, Azure Functions, and Azure App Services, to build and deploy robust, production-ready AI solutions.

MLOps and Governance: Establish and enforce MLOps best practices for the entire AI lifecycle, from continuous integration and deployment (CI/CD) to model monitoring and governance. Ensure all deployments meet enterprise standards for security and reliability.

Stakeholder Collaboration: Work with business leaders and internal stakeholders to gather requirements, identify new use cases, and demonstrate the business value of the chatbot solution. Effectively translate complex technical concepts to non-technical audiences.

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