Lead Generative AI Engineer

Company: 2010 Baker Hughes Limited

Location: IN-KA-BANGALORE-NEON BUILDING WEST TOWER

Type: Full-time

Posted: 2026-07-16

About this role

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 ...

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