Lead Generative AI Engineer

Baker Hughes

. Engineer and deploy production-ready generative AI solutions, including LLMs, VLMs, and multimodal models .

Posted 9/17/2026full-timeBangalore • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and operating generative AI solutions, including LLMs and multimodal models, while implementing MLOps practices and optimizing model performance. Strong collaboration skills are essential for working across engineering and business teams to ensure effective integration and delivery of AI capabilities.

Highest-signal resume keywords

Generative AI Model DeploymentMLOps PracticesPython ProficiencyAI Platform ToolingCloud-Native Architectures

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Generative AI SolutionsModel VersioningFine-TuningPerformance BenchmarkingQuantizationTestingCode ReviewsDocumentationProduction SupportPrompt Optimization

Soft Skills

Problem-SolvingCollaborationCommunicationDelivery-Focused Mindset

Tools & Technologies

PyTorchTensorFlowHugging FaceKubernetesModel RegistriesExperiment TrackingCI/CD PipelinesMonitoring SolutionsVector DatabasesEmbeddings

Certifications & Qualifications

Master’s Degree in Computer SciencePhD in AI or Machine Learning (Preferred)

Industry Keywords

Generative AILLM OpsEvent-Driven ArchitecturesScalable Inference PatternsResponsible AI Standards

Tech Stack

Tools & technologies

CloudKubernetesMicroservicesPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Engineer and deploy production-ready generative AI solutions, including LLMs, VLMs, and multimodal models
Design and operate LLM Ops pipelines covering model versioning, fine-tuning, evaluation, deployment, rollback, and lifecycle management
Build and maintain AI platforms and services supporting prompt management, embeddings, vector search, retrieval-augmented generation, and tool-calling workflows
Integrate generative AI capabilities into enterprise applications using APIs, microservices, and event-driven architectures
Implement MLOps practices including model CI/CD, automated testing, performance benchmarking, observability, logging, and cost monitoring
Optimize model latency, throughput, accuracy, and cost using quantization, caching, batching, and model routing
Collaborate with cloud, data, security, and product teams on enterprise security, governance, and responsible AI standards
Produce technical documentation and operational runbooks
Communicate delivery status and business value to stakeholders
Mentor engineers and contribute to reusable frameworks, standards, and platform capabilities
Build, deploy, and operate generative AI services across the AI lifecycle, from model onboarding and fine-tuning to inference optimization, monitoring, and continuous improvement

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
PhD is a plus, but strong delivery experience is preferred
Proven experience deploying and operating generative AI models in production
Strong proficiency in Python
Practical experience using PyTorch, TensorFlow, Hugging Face, and transformer-based architectures
Experience with AI platform and MLOps tooling, including model registries, experiment tracking, orchestration, CI/CD pipelines, and monitoring solutions
Solid understanding of cloud-native architectures, containers, and scalable inference patterns, such as 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
Ability to work effectively across engineering and business teams
Delivery-focused mindset and comfort owning systems in production and continuously improving them

Benefits

Comp & perks

Working flexible hours
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