Agentic AI Engineer

Role Details Role: Agentic AI Engineer Work Type: Remote Engagement: Part-time Experience: Hands-on experience with Agentic AI / LLM application development Focus Areas: AI Agents, LLMs, RAG, Tool Calling, MCP, Multi-Agent Systems, AI Automation, Code Transformation, Technology Migration & Modernization About the Role We’re looking for a hands-on Agentic AI Engineer to design, build, and improve AI agents, agentic workflows, and intelligent automation systems. You’ll work on practical AI solutions involving LLMs, tool calling, RAG, multi-agent workflows, and AI-driven software modernization. This is a remote, part-time opportunity for someone who enjoys experimenting with emerging AI technologies, solving complex engineering problems, and quickly turning ideas into reliable, production-ready solutions. Responsibilities Design, develop, and deploy AI agents and agentic workflows for real-world business and engineering use cases. Integrate LLMs with APIs, tools, databases, external services, and enterprise systems. Build and optimize RAG pipelines, prompt workflows, knowledge systems, and AI evaluation frameworks. Develop tool/function-calling workflows that enable AI agents to perform multi-step tasks autonomously. Build multi-agent systems where specialized agents collaborate to complete complex workflows. Develop AI-powered automation solutions that improve engineering and business processes. Build AI agents capable of analyzing, transforming, and modernizing existing applications and codebases. Develop workflows for code conversion and technology migration, including migration between programming languages, frameworks, and technology stacks. Prototype and ship AI automation features quickly while maintaining code quality and reliability. Design mechanisms for testing, validation, observability, and evaluation of AI agent outputs. Monitor and continuously improve agent performance, accuracy, reliability, latency, and scalability. Debug complex issues across LLM applications, APIs, agent workflows, databases, and integrations. Stay current with emerging developments in agentic AI, LLMs, AI coding agents, MCP, and AI-powered software engineering. Requirements Strong Python programming skills and solid software engineering fundamentals. Hands-on experience building LLM-powered applications and AI agents. Experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, or similar frameworks. Strong understanding of RAG, vector databases, embeddings, semantic search, and document retrieval. Experience with LLM APIs, tool/function calling, structured outputs, and prompt engineering. Familiarity with OpenAI and/or Anthropic APIs and Model Context Protocol (MCP). Experience integrating AI systems with REST APIs, databases, third-party services, and enterprise applications. Experience building production-grade AI agents, automation systems, or AI-powered applications. Experience developing AI agents for automated code transformation, technology migration, and modernization, including converting applications or codebases between programming languages, frameworks, and technology stacks. Ability to design AI workflows that analyze existing code, generate transformed code, and validate that business logic and functionality are preserved. Strong problem-solving, debugging, and system-design skills. Ability to work independently and take ownership of projects in a remote environment. Strong communication skills and the ability to explain technical concepts clearly. Experience working in a startup or early-stage product environment is a plus. Additional Advantage Knowledge or development experience with .NET/C#, Java, or Mainframe technologies. Experience with legacy application modernization and migration. Experience integrating modern AI solutions with legacy enterprise systems. Experience with cloud platforms such as AWS, Azure, or Google Cloud. Familiarity with containerization and deployment technologies such as Docker and Kubernetes. Experience with CI/CD, automated testing, observability, and production AI deployments. Experience evaluating and benchmarking LLM and agent performance. Understanding of enterprise software architectures and distributed systems. What We’re Looking For We’re looking for someone who is not only familiar with AI frameworks but can build working systems end-to-end. The ideal candidate can take a problem such as: Understand an existing application → analyze its code and dependencies → plan the migration → use AI agents to transform the code → test and validate the output → integrate the modernized application into the target environment. You should be comfortable experimenting with new AI capabilities while also applying strong software engineering, architecture, testing, and debugging practices.