Machine Learning Engineer

About the Company : Headquartered in the Hughes Landing area of The Woodlands just north of Houston, Beusa’s shared services teams closely support our industry-leading family of companies – Evolution Well Services, Dynamis Power Solutions, Accelerated Mobile Power, and Mertz Integration – as well as one another. At Beusa, team members work together to collaborate with integrity, push the boundaries of innovation, and redefine excellence through continuous quality improvement. At Beusa, we invest in those who invest in us. We offer competitive compensation, a distinctive premium health insurance plan, and a 401k program with company matching. Our state-of-the-art office space, equipped with cutting-edge technology, provides a dynamic yet comfortable environment where you can achieve your personal best and contribute to our collective success. About the Role : The Artificial Intelligence Machine Learning 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. Essential Functions: (The following duties and responsibilities are all essential job functions, as defined by the ADA, except for those that begin with the word "may.") 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. Minimum Qualifications
Must be legally authorized to work in the United States without the need for sponsorship.
Must be at least 18 years of age or older.
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).
Daily overtime required and in person, predictable attendance in The Woodlands, TX.
Valid U.S. Driver’s License required. Most employment is contingent upon meeting company driving standards, including 3 year U.S. driving history and an acceptable Motor Vehicle Record (MVR) in accordance with Company policy. Education/Experience Level
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.
Oil & Gas industry experience is a plus. Qualifications, Skills, Competencies, Abilities 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. Pay range and compensation package Compensation details will be discussed during the interview process. Employer-paid Medical, Dental, Vision for employee and dependents AND 401(k) with safe harbor matching. Physical Requirements/Work Environment The physical demands and work environment described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation(s) may be made to enable individuals with disabilities to perform the essential functions. The AI/ML Engineer work indoors in an office setting, primarily sitting for extended periods of time at a desk station, keyboarding and using repetitive motions with wrists, hands, and or fingers. Vision abilities required by this job include close vision and the ability to adjust focus while reading and staring at computer monitor. They also need to speak clearly and audibly, as well as have the ability to hear, understand, and distinguish speech and /or other sounds (e.g., building alarms) deriving from in person speech, telephone, or other remote speech. No adverse environmental conditions are expected. While in the office, the AI/ML Engineer may be called upon to stand, kneel, push, pull, reach overhead, stoop, crouch, climb, and lift; therefore, the AI/ML Engineer should be able to lift 25 lbs. independently. Work hours may include early morning, late evenings, and weekends, depending on business necessity. Equal Opportunity Statement The Company is committed to the cause of equal employment opportunity for all employees and applicants, thus abiding by all applicable state and federal laws. Our practices regarding employment, job promotion, compensation, training, and termination do not discriminate based on race, color, religious creed, age, sex, national origin, veteran's status, disability, pregnancy, genetic information, or any other legally protected status. It is expected that all employees, both management and staff, will fully support these nondiscriminatory policies. The company has reviewed this job description to ensure that essential functions and basic duties have been included. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills, and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate.