GE Healthcare · Bengaluru
Otherposted 4w ago
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 Infrastructure as Code (Terraform, CDK, CloudFormation).
Good understanding of data architecture and distributed systems.
Experience implementing secure and compliant cloud solutions.
Preferred Qualifications
Experience with agentic AI frameworks and multi-agent systems.
Familiarity with LLM orchestration tools (LangChain, LlamaIndex).
Experience with vector databases (Pinecone, OpenSearch, FAISS).
Knowledge of streaming and big data technologies (Kafka, Spark).
AWS certifications:
AWS Certified Solutions Architect – Professional.
AWS Machine Learning Specialty
Experience in enterprise SaaS platforms or regulated industries.
Key Skills & Competencies
Expertise in scalable AI system design
Good foundation in ML, DL, and GenAI
Deep knowledge of MLOps and cloud-native architectures
Excellent problem-solving and system design skills
Good communication and stakeholder management
Ability to balance innovation with enterprise constraints (security, compliance, cost)
Impact of the Role
This role will be instrumental in shaping the organization’s AI platform strategy and execution, enabling rapid development and deployment of next-generation AI solutions, including Generative AI and agentic systems, while ensuring scalability, security, and compliance at enterprise scale.
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 behaviors: 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.
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Relocation Assistance Provided: No