ML/AI Research Engineer — Agentic AI Lab (Founding Team)

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Company Overview

A technology company backed by leading venture capital is building a world-class engineering organization focused on solving critical enterprise AI infrastructure challenges. The organization is developing next-generation infrastructure designed around AI agents, retrieval-augmented generation, knowledge graphs, and multi-tenant governance.

The organization is seeking an ML/AI Research Engineer to join its Agentic AI Lab as part of the founding team. This role will focus on designing, training, evaluating, and optimizing agent-native AI models that form the intelligence layer of an enterprise data platform.

The successful candidate will work at the intersection of large language models, vector search, graph reasoning, and reinforcement learning. The role covers the full machine learning lifecycle, including data curation, model fine-tuning, evaluation, interpretability, deployment, alignment, cost optimization, and agent coordination.

This is a full-cycle ML/AI engineering position rather than a prompt engineering role. The engineer will contribute directly to the development of intelligent systems capable of reasoning over enterprise data and executing complex tasks through agent-based architectures.

Core Responsibilities

• Fine-tune and evaluate open-source large language models such as LLaMA 3, Mistral, Falcon, and Mixtral for enterprise use cases involving structured and unstructured data.
• Build and optimize retrieval-augmented generation (RAG) pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, and Dust.
• Integrate RAG systems with vector databases and internal knowledge graphs to enable advanced enterprise information retrieval.
• Train and optimize agent architectures such as ReAct, AutoGPT, BabyAGI, and OpenAgents using enterprise task data.
• Develop embedding-based memory and retrieval chains using token-efficient chunking and retrieval strategies.
• Create reinforcement learning pipelines to optimize agent behavior using techniques such as RLHF, DPO, and PPO.
• Develop scalable evaluation harnesses for assessing LLM and agent performance.
• Implement synthetic evaluations, trace capture, and explainability tools to improve model and agent assessment.
• Contribute to model observability, drift detection, error classification, and AI alignment initiatives.
• Optimize inference latency and GPU resource utilization across cloud and on-premises environments.
• Collaborate with engineering and research teams to translate enterprise AI requirements into scalable machine learning solutions.
• Help establish best practices for reliable, cost-efficient, and production-ready agentic AI systems.

Technical Focus Areas

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Agentic AI and autonomous agent architectures
• Vector search and embedding systems
• Knowledge graphs and graph reasoning
• Reinforcement learning for AI agents
• Model fine-tuning and evaluation
• AI observability and interpretability
• Model alignment and drift detection
• GPU optimization and inference performance
• Enterprise AI infrastructure

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