Baker Hughes

Lead Generative AI Engineer

Posted Yesterday

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2 Locations

In-Office

Senior level

Energy

The Role

Lead design, build, deploy, and operate production-grade generative AI (LLMs, VLMs, multimodal) and MLOps platforms. Responsibilities include model onboarding, fine-tuning, RAG, vector search, CI/CD for models, scalable inference (Kubernetes), observability, cost/performance optimization, security/governance, documentation, and mentoring engineers.

Summary Generated by Built In

Lead Generative AI Specialist

Are you a highly motivated, creative individual and passionate about sales and proposals?

Would you like to be a part of successful team?

Join our team!

Are you passionate about building production‑grade AI systems that deliver real business value? Do you enjoy designing scalable platforms, deploying large language models, and operating AI services reliably in enterprise environments? Join our Digital Technology Team and help power the next generation of AI‑driven energy solutions.

Our team develops industry‑leading products and services that optimize energy production and processing. In this role, you will focus on hands‑on generative AI engineering, working across model integration, AI platforms, and MLOps to operationalize large‑scale AI solutions that are secure, scalable, and performant.

Partner with the best

As a Generative AI Specialist, you will be embedded in cross‑functional delivery teams, building, deploying, and operating generative AI services in a commercial and industrial context. You will work end‑to‑end across the AI lifecycle—from model onboarding and fine‑tuning to inference optimization, monitoring, and continuous improvement.

As a Lead Generative AI Specialist, you will be responsible for:

Engineering and deploying production‑ready generative AI solutions, including LLMs, VLMs, and multimodal models, with a strong emphasis on inference, scalability, and reliability.
Designing and operating LLM Ops pipelines, including model versioning, fine‑tuning, evaluation, deployment, rollback, and lifecycle management.
Building and maintaining AI platforms and services that support prompt management, embeddings, vector search, retrieval‑augmented generation (RAG), and tool‑calling workflows.
Integrating generative AI capabilities into enterprise applications using APIs, microservices, and event‑driven architectures.
Implementing MLOps best practices, including CI/CD for models, automated testing, performance benchmarking, observability, logging, and cost monitoring.
Optimizing model performance across latency, throughput, accuracy, and cost using techniques such as quantization, catching, batching, and model routing.
Collaborating with cloud, data, security, and product teams to ensure solutions meet enterprise standards for security, governance, and responsible AI.
Producing clear technical documentation and operational runbooks and communicating delivery status and business value to stakeholders.
Mentoring engineers and contributing to reusable frameworks, standards, and platform capabilities.

Fuel Your Passion

To be successful in this role, you will have:

A master’s degree in computer science, AI, Machine Learning, or a related field, or equivalent hands‑on industry experience.

PhD is a plus, but strong delivery experience is preferred.
Proven experience deploying and operating generative AI models in production, rather than only research or experimentation.
Strong proficiency in Python, with practical experience using PyTorch, TensorFlow, Hugging Face, and transformer‑based architectures.
Experience with AI platform and MLOps tooling, such as model registries, experiment tracking, orchestration, CI/CD pipelines, and monitoring solutions.
Solid understanding of cloud‑native architectures, containers, and scalable inference patterns (e.g., Kubernetes‑based deployments).
Hands‑on experience with RAG systems, vector databases, embeddings, prompt optimization, and evaluation frameworks.
Strong software engineering discipline, including testing, code reviews, documentation, and production support.
Excellent problem‑solving, collaboration, and communication skills, with the ability to work effectively across engineering and business teams.
A delivery‑focused mindset, comfortable-owning systems in production and continuously improving them.

Work in a way that works for you

We recognize that everyone is different and that the way in which people want to work and deliver at their best is different for everyone too.

Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive

Working with us

Our people are at the heart of what we do at Baker Hughes. We know we are better when all of our people are developed, engaged and able to bring their whole authentic selves to work. We invest in the health and well-being of our workforce, train and reward talent and develop leaders at all levels to bring out the best in each other.

Working for you

Our inventions have revolutionized energy for over a century. But to keep going forward tomorrow, we know we have to push the boundaries today. We prioritize rewarding those who embrace change with a package that reflects how much we value their input. Join us, and you can expect.

Contemporary work-life balance policies and wellbeing activities

Comprehensive private medical care options

Safety net of life insurance and disability programs

Tailored financial programs

Additional elected or voluntary benefits

The Baker Hughes internal title for this role is: Lead Engineer, Mathematics & Data Science, Disciplinary Engineering and Science

Skills Required

Master's degree in Computer Science, AI, Machine Learning or equivalent hands-on industry experience
PhD in relevant field (preferred)
Proven experience deploying and operating generative AI models in production
Strong proficiency in Python
Practical experience with PyTorch, TensorFlow, Hugging Face, and transformer-based architectures
Experience with AI platform and MLOps tooling (model registries, experiment tracking, orchestration, CI/CD, monitoring)
Solid understanding of cloud-native architectures, containers, and Kubernetes-based deployments
Hands-on experience with RAG systems, vector databases, embeddings, and prompt optimization
Experience integrating AI into enterprise apps via APIs, microservices, and event-driven architectures
Experience optimizing model performance (quantization, caching, batching, model routing) for latency/cost
Strong software engineering discipline: testing, code reviews, documentation, and production support
Ability to mentor engineers and contribute to reusable frameworks and platform capabilities

Baker Hughes Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Baker Hughes and has not been reviewed or approved by Baker Hughes.

Retirement Support — Feedback suggests retirement contributions combine automatic employer funding with a dollar‑for‑dollar match, with immediate vesting on the match. This structure can meaningfully increase savings for employees who participate consistently.
Equity Value & Accessibility — Feedback suggests a discounted employee stock purchase program with regular purchase periods and no brokerage fees provides accessible ownership upside. Program parameters are clearly laid out through the benefits hub and plan materials.
Leave & Time Off Breadth — Feedback suggests exempt staff have flexible, manager‑approved time off while non‑exempt employees accrue vacation and receive paid personal time. Company‑paid holidays and paid parental leave further broaden time‑away options.