- Key Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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

Mandatory

- 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
- Solid 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

Agentic AI Manager – Skill Matrix

SkillPriority

- Agentic AI / AI Agents

- Python

- LLM / Generative AI
- RAG & Vector Search
- Strong Agent Frameworks (LangGraph/LangChain/Semantic Kernel etc.)

📌 Agentic AI Developer (Mumbai)
🏢 VLink
📍 Mumbai