Artificial Intelligence Machine Learning Engineer

Overview

Department:
Data Technology

Job Status:
Full-Time

FLSA Status:
Salary-Exempt

Reports To:

Data Architecture Manager

Location:

Hybrid/The Woodlands, TX

Amount of

Travel Required:

Less than 10%

Work Schedule:

Monday – Friday, 8 a.m. – 5 p.m.

Positions Supervised:
None

AIP:
Level 6

The AI/ML Engineer designs, develops, and deploys Generative AI and traditional machine learning solutions across the BEUSA family of companies. This role focuses on hands-on engineering: building models, data pipelines, and services that integrate with business processes to drive measurable impact. The ideal candidate is an engineer with strong fundamentals in ML/LLMs, solid software craft, and a collaborative mindset. You are comfortable owning features end-to-end, partnering with cross-functional teams, and continuously learning new tools and methods.

The ideal candidate is a highly skilled engineer with deep technical expertise in AI/ML, a passion for Generative AI, and a collaborative mindset. This role requires strong problem-solving skills, the ability to work independently, and a desire to stay at the forefront of AI/ML advancements.

• AI/ML Solution Development:
  Design, implement, and deploy scalable AI/ML models (with emphasis on Generative AI applications such as LLMs, retrieval-augmented generation, and prompt engineering). Build robust data pipelines, feature engineering workflows, and training/evaluation jobs using Python and standard ML libraries. Package and deploy models as services or batch jobs; implement inference pipelines and optimize for latency, throughput, and cost.
• Generative AI Innovation:
  Evaluate and integrate Generative AI models and frameworks (e.g., LLMs, embeddings, vector search, diffusion models) for defined use cases. Develop prompts, RAG pipelines, guardrails, and evaluation harnesses; conduct A/B and offline evaluations to improve output quality and safety.
• MLOps/LLMOps Execution:
  Apply best practices for experiment tracking, model versioning, CI/CD, monitoring, and alerting. Implement data and model quality checks, drift detection, and performance dashboards. Contribute infrastructure-as-code or configuration needed to run training/inference at scale in collaboration with platform teams.
• Data and Systems Integration:
  Integrate AI/ML services with existing data platforms and business systems (APIs, event streams, warehouses, BI). Collaborate with IT and data architecture teams to ensure reliable data access, security, and compliant deployments.
• Stakeholder
  
  Collaboration:
  
  Work closely with product, analytics, and business stakeholders to refine requirements, scope technical tasks, and deliver increments that meet acceptance criteria. Document designs, assumptions, and operational runbooks; communicate progress and trade-offs clearly.
• AI Ethics & Best Practices:
  Implement privacy, security, safety, and fairness considerations in data handling and model behavior consistent with organizational guidelines. Contribute to model evaluation criteria, red-teaming tests, and content filtering aligned with ethical standards.
• Change Advocacy:
  Promote understanding and adoption of AI across all levels of the organization, training stakeholders on AI’s benefits, risks, and ethical implications.
• Infrastructure & Systems Integration:
  Partner with IT and data architecture teams to ensure robust data pipelines and infrastructure, enabling successful deployment and scaling of AI solutions.
• KPI Development & Monitoring:
  Develop and monitor KPIs to track the success of AI initiatives, providing insights on performance, ROI, and opportunities for improvement.
• Continuous Learning:
  Stay up to date on emerging trends in Generative AI and traditional data science to ensure the company adopts cutting-edge methods and tools.

• Successfully passes background check, pre-employment drug screening, and any pre-employment aptitude and/or competency assessment(s).
• Proficiency in spoken English language.
• Daily in-person, predictable attendance.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Mathematics, or a related field.
• 2–5 years…