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 and

Mandatory skill sets: 

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 Experience

Years of experience required: 

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: