Description

Design, build, and deploy autonomous AI agents for multi-step workflow automation. Develop multi-agent architectures using supervisor, hierarchical, swarm, or sequential orchestration patterns. Integrate agents with internal and third-party tools, APIs, databases, search systems, and business applications. Build tool-calling, memory, planning, reasoning, and state-management capabilities for agentic applications. Develop Retrieval-Augmented Generation (RAG) pipelines to provide agents with relevant and trusted business context. Implement agent evaluation frameworks to assess task completion, tool-use accuracy, hallucination rates, latency, and cost. Add safety controls, guardrails, approval workflows, role-based permissions, and fallback mechanisms. Optimize agent workflows for scalability, response quality, latency, and token consumption. Collaborate with product, backend, data, and cloud teams to embed agentic workflows into production platforms. Stay current with agent frameworks, Model Context Protocol (MCP), model routing, and emerging orchestration patterns.

Skill and Ability:

3+ years of experience in software engineering, AI engineering, machine learning, or related roles. Strong proficiency in Python and backend API development using FastAPI, Flask, or Django. Hands-on experience with LLM APIs and agent frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen. Experience building tool-calling workflows and integrating AI systems with REST APIs, databases, queues, and enterprise tools. Strong understanding of RAG, embeddings, vector databases, semantic search, prompt design, and context management. Familiarity with multi-agent orchestration, state handling, workflow execution, and memory patterns. Experience deploying and monitoring AI applications on AWS, GCP, or Azure. Knowledge of LLM observability, testing, evaluation, and AI safety practices. Experience with MCP or other AI-tool integration standards. Experience with Redis, Celery, RabbitMQ, Kafka, or workflow orchestration platforms. Familiarity with Docker, Kubernetes, CI/CD, and LLMOps practices. Experience implementing human-in-the-loop approval mechanisms for autonomous AI tasks

Key Skills

Education

Bachelor's degree