Posted:
5/18/2026, 12:35:20 AM
Location(s):
Bengaluru, Karnataka, India ⋅ Karnataka, India
Experience Level(s):
Senior
Field(s):
AI & Machine Learning ⋅ DevOps & Infrastructure ⋅ Software Engineering
Key Responsibilities:
Define and lead the architecture of AI/ML platforms, MLOps pipelines, and SaaS applications on AWS.
Drive adoption of AI-first and cloud-first principles across engineering teams.
Lead design of scalable systems leveraging GenAI / LLMs, AI Learning Engineering frameworks, Distributed systems and microservices
Establish best practices for secure, scalable, and resilient cloud-native architectures.
Ensure alignment with enterprise architecture and long-term technology strategy.
Build and scale end-to-end AI/ML platforms including Data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
Implement robust MLOps practices: Model versioning, reproducibility, CI/CD for ML, observability, and governance.
Drive development of AI DLC (Deep Learning Containers) and standardized environments for training/inference.
Enable AI automation across pipelines and workflows to accelerate experimentation and deployment.
Lead integration of GenAI capabilities into products and internal platforms.
Oversee development of multi-tenant SaaS platforms with high availability and scalability.
Ensure engineering excellence in API design, Backend systems, Frontend integration (where applicable)
Drive DevOps and CI/CD maturity for rapid, reliable releases.
Champion platform engineering principles for reusable and modular services.
Lead cloud strategy and implementation using AWS services such as Compute (EC2, Lambda, EKS), Storage (S3, EFS), AI/ML (SageMaker, Bedrock), Data (Redshift, Glue, Athena)
Optimize cloud infrastructure for performance, scalability, and cost efficiency.
Establish cloud governance frameworks including security, compliance, and tagging strategies.
Own and drive cost governance strategies across AI and cloud platforms.
Implement FinOps practices: Cost visibility, allocation, and forecasting, Resource optimization (compute, storage, GPU usage)
Continuously optimize Model training/inference costs, Infrastructure utilization, SaaS operational expenses
Build, mentor, and lead high-performing engineering teams across AI, software, and cloud domains.
Foster a culture of Ownership, Innovation, Continuous learning, Collaboration
Provide technical coaching and career development for engineers and managers.
Drive hiring strategies to attract top AI and cloud talent.
Lead initiatives in AI Learning Engineering: Continuous model improvement, Feedback loops, Human-in-the-loop systems
Promote experimentation with GenAI, LLMs, and emerging AI technologies.
Translate research and innovation into production-ready solutions.
Apply first-principles thinking to decompose complex technical and business problems.
Drive data-driven decision-making and engineering trade-offs.
Lead teams in solving ambiguous, high-impact challenges with clarity and rigor.
Identify opportunities for AI-driven automation across Development workflows, Testing and QA, Infrastructure management, Customer-facing features
Build intelligent systems that reduce manual effort and improve productivity.
Required Qualifications:
15+ years of experience in software engineering, cloud engineering, or AI/ML systems.
5+ years in technical leadership or engineering management roles.
Strong experience with AWS cloud ecosystem, AI/ML platforms and MLOps, SaaS architecture and delivery models
Hands-on experience with CI/CD pipelines and DevOps practices.
Proven track record of leading large-scale distributed systems.
Experience with GenAI / LLM frameworks and applications.
Familiarity with AI Software Development Life Cycle, GPU workloads, and deep learning infrastructure.
Knowledge of Kubernetes, containerization, and platform engineering.
Exposure to FinOps and cost optimization strategies.
Experience in regulated industries (e.g., healthcare, finance) is a plus.
AI-First Mindset – Prioritize AI-driven solutions and innovation.
Cloud-First Mindset – Design for scalability, resilience, and elasticity.
Strategic Thinking – Align engineering efforts with business goals.
Execution Excellence – Deliver high-quality solutions at scale.
Leadership & Influence – Inspire teams and drive cross-functional collaboration.
Problem-Solving – Strong analytical and first-principles thinking approach.
Cost Awareness – Balance innovation with financial efficiency.
Inclusion and Diversity
GE Healthcare is an Equal Opportunity Employer where inclusion matters. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
We expect all employees to live and breathe our behaviours: to act with humility and build trust; lead with transparency; deliver with focus, and drive ownership – always with unyielding integrity.
Our total rewards are designed to unlock your ambition by giving you the boost and flexibility you need to turn your ideas into world-changing realities. Our salary and benefits are everything you’d expect from an organization with global strength and scale, and you’ll be surrounded by career opportunities in a culture that fosters care, collaboration and support.
#Everyroleisvital
#LI-SM1
#Hybrid
Relocation Assistance Provided: No
Website: https://www.gehealthcare.com/
Headquarter Location: Chicago, Illinois, United States
Employee Count: 10001+
Year Founded: 1994
IPO Status: Public
Last Funding Type: Post-IPO Secondary
Industries: Apps ⋅ Health Care ⋅ Health Diagnostics ⋅ Home Improvement ⋅ Home Renovation ⋅ Internet ⋅ Medical