Machine Learning Engineer (GenAI)

Harnham; Houston (TX); Híbrido; USD 162.000 - 198.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

Professional development

Descripción de la vacante

Harnham in Houston is seeking an experienced Machine Learning Engineer focused on Generative AI and Agentic AI engineering. You will own design, development and deployment of advanced ML and agentic applications, delivering scalable AI infrastructure and intelligent automation.

You will lead the end-to-end lifecycle from experimentation to production, building robust inference pipelines and monitoring systems across the ML stack.

Formación

• 5+ years of experience in Machine Learning Engineering, with Generative AI focus.
• Experience deploying ML and GenAI models into production.
• Strong foundations in NLP, DL, Transformer architectures.

Responsabilidades

• Design, build and deploy production-grade ML models and agentic AI apps.
• Engineer autonomous AI workflows for document processing and NLP tasks.
• Develop and optimise LLM-powered applications with RAG and embeddings.
• Evaluate and deploy open-source or commercial foundation models; select architectures.

Conocimientos

GenAI

LLMs

Agentic AI

Production ML

NLP

Transformer

Model deployment

RAG pipelines

Herramientas

Vector databases

RAG frameworks

Retrieval systems

Descripción del empleo

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.