Senior Staff Solution Architect - AI & Cloud

gehc; Bengaluru; On-site; INR 3,500,000 - 7,500,000; Full time

Job summary

GE Healthcare is seeking a Senior Staff AI & Cloud Solution Architect to lead scalable AI platforms on AWS. You will own end-to-end AI pipelines, ensure SaaS readiness, and drive GenAI initiatives across the enterprise. The role demands 12+ years in software engineering, AI/ML, and cloud architecture, with strong AWS and MLOps expertise.

You will mentor teams, define best practices, and shape secure, compliant AI solutions for a healthcare tech leader.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field (PhD preferred) with a minimum of 12+ years of experience in software engineering, AI/ML, and cloud architecture.
• Good expertise in Machine Learning Engineering and Deep Learning Engineering.
• Hands-on experience with AWS (SageMaker, Bedrock, core cloud services).
• MLflow and MLOps frameworks.
• Good programming skills in Python and ML frameworks (PyTorch, TensorFlow, etc.).
• Proven experience designing and deploying scalable AI systems in production.

Responsibilities

• Design and build scalable, multi-tenant AI platforms on AWS supporting diverse ML and GenAI use cases.
• Define reference architectures, reusable components, and best practices for AI/ML systems.
• Architect end-to-end AI pipelines spanning data ingestion, feature engineering, model training, deployment, and monitoring.
• Ensure platform readiness for SaaS environments, including tenant isolation, scalability, and cost optimization.
• Lead development of machine learning and deep learning models for production-grade applications.
• Establish standards for ML engineering, including data preprocessing, feature stores, training pipelines, and inference services.
• Optimize model performance, scalability, and latency for real-time and batch inference systems.
• Design and implement Generative AI solutions using AWS Bedrock and foundation models.
• Build RAG pipelines with vector databases and embeddings.
• Develop agentic AI systems, including autonomous agents, orchestration frameworks, and tool integration.
• Apply advanced techniques such as prompt engineering, fine-tuning, evaluation frameworks, and guardrails.
• Architect and implement AWS solutions (SageMaker, Bedrock, etc).
• Build highly available, fault-tolerant, and cost-optimized cloud-native systems.
• Implement Infrastructure as Code (IaC) using Terraform, AWS CDK, or CloudFormation.
• Establish and scale MLOps frameworks for CI/CD of ML models.
• Enable model monitoring, drift detection, retraining pipelines, and explainability.
• Design scalable data architectures to support AI/ML workloads and data governance.

Skills

AI/ML engineering

Cloud architecture

MLOps

Generative AI

Python

Education

Bachelor's/Master's in CS/AI/Data Science

PhD preferred

Tools

AWS SageMaker

AWS Bedrock

MLflow

Python

PyTorch/TensorFlow

Job description

Job Description Summary

We are looking for a highly skilled Senior Staff AI & Cloud Solution Architect to lead the design and development of scalable AI platforms and intelligent solutions on AWS. This role requires deep expertise across AI/ML engineering, cloud architecture, MLOps, and Generative AI, along with strong experience in building enterprise-grade, secure, and compliant SaaS platforms.

You will be responsible for architecting end-to-end AI systems, enabling machine learning and deep learning engineering workflows, and driving innovation in GenAI and agentic AI solutions. This role sits at the intersection of platform engineering, data architecture, and applied AI, with a strong focus on scalability, reliability, and governance.

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.

Job Description

Key Responsibilities

AI Platform & Solution Architecture

• Design and build scalable, multi-tenant AI platforms on AWS supporting diverse ML and GenAI use cases.
• Define reference architectures, reusable components, and best practices for AI/ML systems.
• Architect end-to-end AI pipelines spanning data ingestion, feature engineering, model training, deployment, and monitoring.
• Ensure platform readiness for SaaS environments, including tenant isolation, scalability, and cost optimization.

Machine Learning & Deep Learning Engineering

• Lead development of machine learning and deep learning models for production-grade applications.
• Establish standards for ML engineering, including data preprocessing, feature stores, training pipelines, and inference services.
• Optimize model performance, scalability, and latency for real-time and batch inference systems.

Generative AI & Agentic AI Systems

• Design and implement Generative AI solutions using AWS Bedrock and foundation models.
• Build RAG (Retrieval-Augmented Generation) pipelines with vector databases and embeddings.
• Develop agentic AI systems, including autonomous agents, orchestration frameworks, and tool integration.
• Apply advanced techniques such as prompt engineering, fine-tuning, evaluation frameworks, and guardrails.

AWS Cloud Architecture

• Architect and implement solutions using:
  • AWS SageMaker (training, pipelines, deployment)
  • AWS Bedrock (GenAI services)
  • Compute: Lambda, ECS, EKS
  • Orchestration: Step Functions
  • Storage/Data: S3, Redshift, DynamoDB, Glue
• Build highly available, fault-tolerant, and cost-optimized cloud-native systems.
• Implement Infrastructure as Code (IaC) using Terraform, AWS CDK, or CloudFormation.

MLOps & Model Lifecycle Management

• Establish and scale MLOps frameworks for continuous integration and deployment of ML models.
• Implement MLflow for experiment tracking, model registry, and lifecycle management.
• Build automated CI/CD pipelines for ML workflows.
• Enable model monitoring, drift detection, retraining pipelines, and explainability.

Data Architecture & Engineering

• Design scalable data architectures to support AI/ML workloads (batch and streaming).
• Implement data pipelines, feature stores, and data governance frameworks.
• Ensure data quality, lineage, and accessibility across teams.

Security, Compliance & Governance

• Architect AI systems with security-first principles, including encryption, IAM, and network controls.
• Ensure compliance with enterprise and regulatory standards (e.g., HIPAA, GDPR, SOC2 as applicable).
• Implement responsible AI practices, including bias detection, auditability, and explainability.
• Establish governance for data, models, and AI services.

SaaS Platform Engineering

• Design and implement AI-powered SaaS platforms, supporting:
  • Multi-tenancy and tenant isolation
  • API-driven architectures
  • Usage metering and cost attribution
  • Enable platform extensibility and integration with enterprise systems.

Leadership & Collaboration

• Provide technical leadership and mentorship across AI, ML, and cloud engineering teams.
• Collaborate with product, engineering, and business stakeholders to translate requirements into scalable solutions.
• Lead architecture reviews, design discussions, and technical decision-making.
• Drive innovation and adoption of emerging AI paradigms.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field (PhD preferred) with a minimum of 12+ years of experience in software engineering, AI/ML, and cloud architecture.
• Good expertise in Machine Learning Engineering and Deep Learning Engineering.
• Hands-on experience with:
  • AWS (SageMaker, Bedrock, core cloud services)
  • MLflow and MLOps frameworks
  • Good programming skills in Python and ML frameworks (PyTorch, TensorFlow, etc.).
• Proven experience designing and deploying scalable AI systems in production.
• Experience with In