IN_Manager_ Agentic AI Developer_GCC_Advisory_Bangalore

Job Description & Summary

We are looking for an experienced Agentic AI Developer to design, build, and deploy intelligent autonomous agent systems using Python. You will be responsible for architecting multi-agent workflows, integrating LLMs with real-world tools and data sources, and building the orchestration and reasoning layers that power self-directed AI pipelines. This role sits at the intersection of AI engineering, backend development, and system design.

Responsibilities

• Agent Design & Orchestration - Design and develop autonomous and semi-autonomous AI agents capable of multi-step reasoning, planning, and decision-making
• Build multi-agent systems with defined roles, inter-agent communication, and collaborative task execution
• Implement agent orchestration frameworks (LangGraph, AutoGen, CrewAI, or custom-built) for workflow coordination
• Define and manage agent memory layers - short-term (context window), long-term (vector stores), and episodic memory
• Develop tool-use and function-calling pipelines enabling agents to interact with APIs, databases, and external services
• LLM Integration & Prompt Engineering - Integrate and fine-tune interactions with LLM providers (OpenAI, Anthropic, Mistral, open-source models via HuggingFace) - Design robust prompt templates, system instructions, and chain-of-thought strategies for reliable agent behavior - Implement RAG (Retrieval-Augmented Generation) pipelines connecting agents to structured and unstructured knowledge bases - Manage LLM output validation, structured output parsing, and fallback/retry logic
• Backend Services & API Layer - Build FastAPI-based microservices to expose agent capabilities as REST/streaming APIs
• Develop event-driven agent triggers using message queues (Redis Streams, Kafka, RabbitMQ)
• Design agent state management and session persistence using PostgreSQL / Redis
• Implement task queuing, scheduling, and async execution for long-running agent workflows
• Tool & Data Integration - Build and register custom agent tools for file processing, web search, SQL querying, SFTP operations, and third-party API calls
• Integrate agents with structured data sources (PostgreSQL, data lakes) and unstructured sources (PDFs, Excel, emails)
• Connect agents to vector databases (Pinecone, Weaviate, pgvector, ChromaDB) for semantic retrieval
• Enable agents to interact with external systems via MCP (Model Context Protocol) or custom tool registries
• Guardrails, Safety & Reliability - Implement guardrails and output validators to ensure agent responses meet business and compliance requirements
• Build human-in-the-loop (HITL) checkpoints for critical decision nodes within agent workflows
• Design agent sandboxing and execution boundaries to prevent unintended actions
• Develop comprehensive error handling, retry strategies, and graceful degradation patterns for agent failures
• Observability & Evaluation - Instrument agents with end-to-end tracing (LangSmith, Arize, custom logging) across reasoning steps and tool calls
• Build evaluation pipelines to measure agent accuracy, latency, tool-use correctness, and goal completion rates
• Define and monitor KPIs for agent performance - task success rate, hallucination rate, escalation frequency
• Maintain audit logs of agent decisions, tool invocations, and LLM interactions for debugging

Mandatory skill sets

• Python (advanced) - async programming, OOP, design patterns
• LangChain
• LangGraph
• AutoGen
• CrewAI
• OpenAI API
• HuggingFace Transformers
• FastAPI
• PostgreSQL
• Redis

Preferred skill sets

Experience with fine-tuning or RLHF pipelines for domain-specific LLMs - Familiarity with Model Context Protocol (MCP) for standardized tool integration - Knowledge of open-source LLM deployment (Ollama, vLLM, LiteLLM) - Exposure to Kubernetes-based microservice deployment for agent services - Background in data engineering or ingestion platforms complementing agent data access - Understanding of AI safety principles, responsible AI, and model risk management - Experience with browser-use or computer-use agents for UI automation tasks

Years of experience

• 3+ years of overall software/data/AI engineering experience
• 1-2 years of hands-on experience building LLM-based applications or AI agent systems
• Demonstrated experience shipping production-grade agentic workflows, not just prototypes
• Strong foundation in backend system design with event-driven and microservices architecture
• Prior exposure to enterprise AI use cases such as document processing, workflow automation, data enrichment, or conversational AI

Education qualification

• Bachelors degree in Computer Science, Information Technology, or a related field
• Proven experience of 3+ years with Automation Anywhere and other RPA tools.
• Strong analytical and problem-solving skills.
• Effective communication skills to work with various stakeholders.
• Ability to work independently as well as in a team environment.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.