Artificial Intelligence Machine Learning Engineer

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.

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.

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.