Agentic AI Developer

Vlink; Mumbai; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Vlink is seeking an experienced AI/ML engineer to design end-to-end AI agents and RAG pipelines. You will architect single and multi-agent systems, define goals, tools, memory, and failure handling for enterprise-scale use cases.

You will implement agentic RAG patterns with vector databases, integrate with SAP, Snowflake, and Microsoft 365 APIs, and build MCP-based tool exposure for real-time data access. Apply governance and collaborate with IT and data teams.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field.
• Hands-on experience building at least one production-grade AI agent or LLM-powered application.
• Strong Python programming skills with proficiency in LLM libraries and API integration.
• Experience with at least one agent framework.
• Understanding of RAG architecture and vector search fundamentals.

Responsibilities

• Design end-to-end AI agent architectures including single-agent and multi-agent patterns (ReAct, Reflection).
• Define agent goals, tools, memory strategies, and failure-handling logic.
• Build agentic RAG systems with vector search and LLM reasoning for knowledge-grounded responses.
• Develop AI agents using Python-based frameworks; integrate with enterprise APIs (SAP, Snowflake, Microsoft Graph, SharePoint).
• Implement MCP servers to expose tools and data sources to agents; build real-time tool-calling layers for databases and ERPs.
• Create governance: guardrails, safety, and responsible AI; monitor KPIs and observability for deployments.

Skills

Python

LLM libraries

API integration

Agent frameworks

RAG architecture

Education

Bachelor's degree in Computer Science or related field

Tools

LangGraph

LangChain

Semantic Kernel

Pinecone

Azure AI Search

Job description

Key Responsibilities

• 1. Agent Design & Architecture
  • Design end-to-end AI agent architectures including single-agent and multi-agent systems using patterns such as ReAct, Reflection Pattern.
  • Define agent goals, tools, MCP, memory strategies (in-context, external, semantic, episodic), and failure-handling logic.
  • Select the appropriate agent pattern and framework based on use-case complexity and enterprise constraints
  • Build agentic RAG systems combining vector search, semantic retrieval, and LLM reasoning for knowledge-grounded responses
• 2. Development & Integration
  • Develop AI agents using Python-based frameworks for code-first builds
  • Build and configure agents on Microsoft Copilot Studio with custom connectors, adaptive cards, and Power Automate flow integrations
  • Integrate agents with enterprise APIs: SAP, Snowflake (Cortex), Microsoft Graph API, SharePoint, Teams, and other M365 services etc.
  • Implement MCP (Model Context Protocol) servers to expose tools and data sources to agents in a standardized, composable manner
  • Build tool-calling layers that allow agents to interact with databases, ERPs, and third-party APIs in real time
  • Design human-in-the-loop checkpoints for approval workflows, exceptions, and escalation scenarios
• 3. RAG & Knowledge Management
  • Design and implement Retrieval-Augmented Generation (RAG) pipelines: document ingestion, chunking, embedding, vector store indexing, and context-aware retrieval
  • Work with vector databases (Pinecone, Azure AI Search, ChromaDB) to build semantic search capabilities for enterprise knowledge bases
  • Implement agentic RAG patterns including query routing, self-reflection, and query reformulation for higher-quality responses
• 4. Evaluation, Testing & Observability
  • Build evaluation frameworks to measure agent accuracy, task completion rate, hallucination rate, and latency
  • Implement observability and tracing to debug and improve agent behavior in production
  • Define and monitor KPIs for deployed agents; establish feedback loops for continuous improvement
  • Conduct structured prompt engineering and system prompt optimization to improve reliability and output quality
• 5. Governance, Safety & Responsible AI
  • Implement guardrails, content filters, and safety mechanisms to ensure agents behave within defined boundaries
  • Apply responsible AI principles: fairness, transparency, explainability, and data privacy compliance
  • Document agent logic, decision flows, tool schemas, and known limitations for audit and knowledge transfer
• 6. Collaboration & Stakeholder Management
  • Partner with business process owners to translate operational problems into agent design briefs
  • Collaborate with IT, data engineering, and enterprise architecture teams for system integration and cloud deployment
  • Present agent capabilities, prototypes, and performance reports to senior leadership including CIO-level audiences
  • Mentor junior team members and Management Trainees on agent concepts, tools, and development best practices

Qualifications

• 36 years of experience in software development, AI/ML engineering, or intelligent automation
• Hands-on experience building at least one production-grade AI agent or LLM-powered application
• Strong Python programming skills with demonstrated proficiency in LLM libraries and API integration
• Experience with at least one agent framework
• Understanding of RAG architecture and vector search fundamentals
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field

SkillPriority

• Agentic AI / AI Agents
• Python
• LLM / Generative AI
• RAG & Vector Search
• Strong Agent Frameworks (LangGraph/LangChain/Semantic Kernel etc.)