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Lead Generative AI Engineer
Baker Hughes. Engineering and deploying production‑ready generative AI solutions, including LLMs, VLMs, and multimodal models, with a strong emphasis on inference, scalability, and reliability .
Tech Stack
Tools & technologiesCloudKubernetesMicroservicesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- A master’s degree in computer science, AI, Machine Learning, or a related field, or equivalent hands‑on industry experience
- 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.
Benefits
Comp & perks- 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
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
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Hard Skills & Tools
generative AILLMsVLMsmultimodal modelsMLOpsPythonPyTorchTensorFlowHugging FaceKubernetes
Soft Skills
problem-solvingcollaborationcommunicationmentoringdelivery-focused mindset
Certifications
master’s degree in computer sciencemaster’s degree in AImaster’s degree in Machine Learning