Sr Associate_Agentic AI Developer using Python_GCC_Advisory

Job 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
• 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 and compliance

Mandatory skill sets

• Programming Python (advanced) - async programming, OOP, design patterns
• Agent Frameworks LangChain, LangGraph, AutoGen, CrewAI, or equivalent LLM APIs
• OpenAI API, Anthropic Claude, HuggingFace Transformers API
• Development FastAPI, REST, Streaming APIs (SSE / WebSockets)
• Prompt Engineering CoT, ReAct, structured outputs, function calling
• Data Handling Pandas, structured/unstructured data parsing
• Messaging Events Redis, Kafka, or RabbitMQ for event-driven agent triggers
• Databases PostgreSQL for state/session management
• Observability Logging, tracing (LangSmith / OpenTelemetry), metrics

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

4-7

Education

BE/Btech/MCA/Mtech/MBA

Education details

Degrees/Field of Study required: Bachelor of Technology

Certifications

(if blank, certifications not specified)

Required Skills

Agentic AI

Optional Skills

Accepting Feedback

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