Machine Learning Engineer (GenAI) — Harnham, Greater Houston

Machine Learning Engineer – Agentic AI

Location: Houston, TX – Hybrid (4 days onsite)

Salary: Up to $180,000 base

We're looking for an experienced Machine Learning Engineer with a strong background in production ML systems who has evolved into Generative AI and Agentic AI engineering.

You'll join a growing technical AI team, taking ownership of the design, development and deployment of sophisticated machine learning and agentic applications. This is a hands-on engineering position focused on taking models from experimentation through to production, building scalable AI infrastructure and delivering intelligent automation solutions.

The Role

• Design, build and deploy production-grade machine learning models and agentic AI applications, taking ownership of the full ML engineering lifecycle.

• Engineer autonomous AI workflows for complex document processing, NLP, information extraction and business process automation.

• Develop and optimise LLM-powered applications, incorporating RAG pipelines, embedding strategies, vector databases and external tool integrations.

• Evaluate, integrate and deploy a combination of open-source and commercial foundation models, selecting the right architectures and technologies for different use cases.

• Build scalable inference and model-serving infrastructure, ensuring AI applications are reliable, performant and production-ready.

• Work across the engineering lifecycle, from model experimentation and prototyping through to deployment, monitoring and continuous optimisation.

The Requirements

• 5+ years of experience in Machine Learning Engineering, with recent experience focused on Generative AI, LLMs and Agentic AI systems.

• Strong foundations in traditional machine learning, NLP, deep learning and Transformer architectures.

• Proven experience deploying ML and GenAI models into production environments.

• Hands-on expertise developing RAG architectures, embedding pipelines, vector search and retrieval systems.

• Experience building agentic workflows, integrating external tools and developing autonomous multi-step AI applications.

• Strong understanding of model evaluation, inference optimisation, deployment infrastructure and technology selection.

• Experience with fine-tuning open-source or commercial models would be advantageous.