Job ID: 11570
Location: Houston, Texas
Employment Type: Direct Hire
Date Added: 01/26/2026
Job Description
Location: Remote in USA
Description
As a Machine Learning & Data Engineer, you will leverage our extensive, structured leadership dataset to build advanced AI solutions/agents and create new RAG based solutions. You’ll contribute to the evolution of our Leadership Intelligence Platform, help drive research initiatives, and collaborate with academic partners. You will also support both data engineering and AI engineering efforts, ensuring systems, pipelines, and models work cohesively to power leadership insights at scale.
Responsibilities
- Design, build, and extend ML models (LLMs and traditional ML) that deliver high-accuracy insights from our structured leadership dataset; own end-to-end experimentation, evaluation, and deployment.
- Develop RAG-based agents and algorithms to unlock novel leadership insights from our research database.
- Integrate advanced solutions and AI Agents into the Leadership Intelligence Platform and partner cross-functionally to align features with strategic objectives and user needs.
- Optimize data pipelines and workflows to ensure robust, efficient data ingestion, transformation, and model serving across engineering teams.
- Collaborate on research with academic partners and contribute to publications and thought leadership by validating findings with rigorous methods.
You Bring
- 5+ years of ML engineering experience building and shipping large-scale models and systems (training, tuning, inference, MLOps, monitoring).
- Hands-on expertise with RAG frameworks and LLMs, including designing retrieval strategies, prompt orchestration, evaluation, and deployment at scale.
- Experience building AI agents via the LangChain, LangGraph framework is a plus.
- Strong data engineering fundamentals across pipelines, data quality, and feature engineering to support reliable ML workflows. Experience with Databricks and Azure is a plus.
- Security and privacy mindset, with experience applying best practices to protect sensitive data in ML systems.
- Collaborative, remote-first working style with clear communication and ownership; familiarity with Salesforce (SFDC), Jira, Confluence, and Git.
