Job Title: AI Agent Engineer Experience: 3–5 Years Job Type: Contract Location: USA – Remote Industry: Artificial Intelligence / Generative AI

Key Responsibilities

Design and develop AI agents using LLMs and agentic AI frameworks. Build autonomous and semi-autonomous agents for business process automation. Implement tool calling, function calling, reasoning, planning, and task execution workflows. Develop multi-agent systems that coordinate tasks across specialized AI agents. Integrate AI agents with REST APIs, enterprise applications, databases, and external tools. Build RAG pipelines using embeddings, vector databases, and enterprise knowledge sources. Develop prompt templates, structured outputs, memory management, and context-handling strategies. Implement human-in-the-loop approval workflows for sensitive or high-impact actions. Evaluate agent accuracy, reliability, latency, and cost using appropriate testing frameworks. Implement safeguards against prompt injection, unauthorized tool use, and unsafe outputs. Deploy AI agent applications using Docker, cloud platforms, and production APIs. Monitor agent execution, troubleshoot failures, and optimize performance. Collaborate with software engineers, product managers, data scientists, and AI researchers. Required Technical Skills Strong programming experience in Python. Hands-on experience with LLMs and generative AI applications. Experience with agent frameworks such as LangGraph, LangChain, CrewAI, or AutoGen. Knowledge of prompt engineering, tool calling, and structured output generation. Experience integrating REST APIs, databases, and third-party services. Understanding of RAG, embeddings, semantic search, and vector databases. Familiarity with OpenAI API, Anthropic API, or open-source language models. Experience with FastAPI, Flask, or similar backend frameworks. Knowledge of Git, testing, debugging, and software engineering best practices. Understanding of authentication, API security, and responsible AI development.

Preferred Qualifications

Experience building multi-agent orchestration systems. Familiarity with vector databases such as Pinecone, Weaviate, Milvus, or FAISS. Experience with model evaluation and observability tools such as LangSmith or Arize Phoenix. Knowledge of AWS, Azure, or Google Cloud Platform. Experience with Docker, Kubernetes, and CI/CD pipelines. Familiarity with MCP (Model Context Protocol) and agent-to-tool integrations. Experience with workflow automation platforms and event-driven architectures. Knowledge of LLM fine-tuning, model serving, and inference optimization. Experience implementing agent memory, state persistence, retries, and failure recovery. Education Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Equivalent practical experience may be considered.