Job Description Summary : We are seeking a highly experienced and visionary Senior Manager - AI, Software & Cloud Engineering to lead the design, development, and scaling of next-generation AI-first, cloud-first platforms. This role will drive the strategy and execution of AI/ML platforms, MLOps, GenAI capabilities, and SaaS products on AWS, while building high-performing engineering teams and fostering a culture of innovation, automation, and cost efficiency.

GE Healthcare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world.

Key Responsibilities :

- 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 .- 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 .

Educational Qualifications :

- Bachelor’s or master’s degree in computer science, Engineering, or related field.

Required Qualifications :

- 15+ years of experience in software engineering, cloud engineering, or AI/ML systems .
- 5+ years in technical leadership or engineering management roles .
- Robust 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 .

Preferred skills :

- 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.

Key Competencies :

- 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 - De

📌 Senior Manager - Ai & Cloud Engineering (Bengaluru)
🏢 GE Healthcare
📍 Bengaluru