GenAI Engineer

The Machine Learning Company (TMLC) is hiring GenAI Engineers to design, build and deploy enterprise-grade applications powered by Large Language Models and Agentic AI. You will work on production use cases involving Retrieval-Augmented Generation, intelligent agents, multi-step workflows, enterprise search, document intelligence and AI-powered automation.

What you'll do

• Design and develop LLM-powered applications using commercial and open-source models.
• Build production-grade RAG pipelines, including ingestion, chunking, embedding, retrieval, reranking and generation.
• Develop autonomous and human-in-the-loop AI agents capable of planning, reasoning, tool usage and workflow execution.
• Build single-agent and multi-agent systems using frameworks such as LangGraph, LangChain, CrewAI or similar platforms.
• Integrate agents with APIs, databases, enterprise applications and external tools.
• Implement prompt engineering, structured outputs, function calling and context-management strategies.
• Develop reusable tools, agent workflows, memory systems and orchestration components.
• Build evaluation pipelines for retrieval quality, answer accuracy, hallucination, latency and agent performance.
• Implement observability, tracing, guardrails, access controls and failure-handling mechanisms.
• Deploy scalable GenAI applications using Python, FastAPI, Docker and cloud platforms.
• Collaborate with product, engineering and business teams to convert enterprise requirements into production-ready AI solutions.

What we're looking for

• 2–5 years of software engineering, machine learning or AI development experience.
• Strong proficiency in Python and backend application development.
• Hands-on experience building applications using LLMs such as OpenAI, Azure OpenAI, Gemini, Claude or open-source models.
• Experience with RAG, vector databases and semantic search.
• Experience with LangChain, LangGraph, CrewAI, Semantic Kernel or similar orchestration frameworks.
• Strong understanding of AI agents, tool calling, workflow orchestration and state management.
• Experience integrating REST APIs, databases and third-party applications.
• Familiarity with FastAPI, Git, Docker and cloud deployment.
• Understanding of prompt engineering, embeddings, context windows and model limitations.
• Ability to write clean, modular and production-quality code.

Bonus points

What we offer

• Competitive compensation aligned with impact
• Work alongside Kaggle Grandmasters and top AI researchers on frontier problems
• Real ownership and surface area — outcomes, not tickets
• Hybrid flexibility and a learning-first culture via TMLC Academy