Artificial Intelligence Machine Learning Engineer

Beusa Energy Group•The Woodlands, TX

2d•Onsite

About The Position

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.

Requirements

Successfully passes all applicable general pre-employment testing including but not limited to: background check, pre-employment drug screening, pre-employment fit tests, pre-employment aptitude and/or competency assessment(s).
Proficiency in the spoken English language
Position requires in-person, predictable attendance
Valid U.S. Driver’s License required. Employment is contingent upon meeting the company's driving standards, including an acceptable Motor Vehicle Record (MVR) in accordance with Company policy.
Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Mathematics, or a related field.
2–5 years of professional experience developing and deploying machine learning models in production.
1+ year of hands-on experience implementing Generative AI solutions in production or pilot environments.
Experience with Databricks or similar data/ML platforms.
Technical Expertise: Proficiency in Python and common ML/AI libraries and tools (e.g., scikit-learn, PyTorch or TensorFlow, Transformers, LangChain/LlamaIndex or equivalent).
Practical experience with LLMs and Generative AI (prompt engineering, RAG, embeddings, vector databases, safety/guardrails, evaluation).
Working knowledge of MLOps best practices: experimentation, versioning, CI/CD, containerization, monitoring, and observability.
Experience deploying in cloud environments (AWS, Azure, or GCP) and using services relevant to data/ML (e.g., serverless, Kubernetes, managed ML services).
Ability to design and optimize data pipelines (batch/stream) and model serving workflows.
Business & Communication Skills: Excellent verbal and written communication skills, with the ability to present technical topics to both technical and non-technical audiences.
Proven ability to work independently, manage multiple priorities, and deliver results in a fast-paced environment.
Proven ability to break down requirements, estimate work, manage priorities, and deliver in a fast-paced environment.
Experience collaborating with cross-functional teams to deliver business-driven AI/ML solutions.
Team-oriented, proactive, and detail-driven with a focus on measurable business outcomes.
Curiosity & Growth Mindset: A high degree of curiosity, with the ability and desire to learn new skills both on-the-fly and in formal learning environments.

Nice To Haves

Oil & Gas industry experience is a plus.

Responsibilities

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 the 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.
Perform other related duties as assigned to assist with successful operations and business continuity.