Senior Staff Solution Architect - AI & Cloud

I00M05 Wipro GE Healthcare Private Limited; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

I00M05 Wipro GE Healthcare Private Limited is looking for a Senior Staff AI & Cloud Solution Architect to design and develop scalable AI platforms. The ideal candidate will have over 12 years of experience and expertise in robust AI/ML engineering and cloud architecture.

This role requires strong skills in building secure SaaS platforms, applying MLOps, and ensuring scalability and compliance. The successful individual will collaborate across teams to drive innovation in AI solutions.

Qualifications

• 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 and MLOps frameworks.
• Experience designing and deploying scalable AI systems in production.

Responsibilities

• Design and build scalable AI platforms on AWS for ML and GenAI.
• Lead development of production-grade machine learning models.
• Establish MLOps frameworks for CI/CD of ML models.
• Implement Infrastructure as Code for cloud solutions.

Skills

AI and Cloud Solution Architecture

Machine Learning Engineering

Deep Learning Engineering

Generative AI

Python programming

MLOps frameworks

Data architecture

Infrastructure as Code

Education

Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field

Tools

AWS (SageMaker, Bedrock, core services)

Terraform

ML frameworks (PyTorch, TensorFlow)

Job description

Job Description Summary

Senior Staff AI & Cloud Solution Architect to design and develop scalable AI platforms and intelligent solutions on AWS. The role requires deep expertise in AI/ML engineering, cloud architecture, MLOps, and Generative AI, with strong experience building enterprise‑grade, secure, and compliant SaaS platforms.

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 production‑grade machine learning and deep learning models.
  • Establish standards for 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 pipelines with vector databases and embeddings.
  • Develop agentic AI systems, including autonomous agents, orchestration frameworks, and tool integration.
  • Apply prompt engineering, fine‑tuning, evaluation frameworks, and guardrails.
• AWS Cloud Architecture
  • Architect and implement solutions using AWS SageMaker, Bedrock, Lambda, ECS, EKS, and Step Functions.
  • Build highly available, fault‑tolerant, and cost‑optimized cloud‑native systems.
  • Implement Infrastructure as Code 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).
  • 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, tenant isolation, API‑driven architectures, usage metering, cost attribution, and extensibility.
• 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

1. 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.
2. Good expertise in Machine Learning Engineering and Deep Learning Engineering.
3. Hands‑on experience with AWS (SageMaker, Bedrock, core cloud services) and MLOps frameworks.
4. Programming skills in Python and ML frameworks (PyTorch, TensorFlow, etc.).
5. Proven experience designing and deploying scalable AI systems in production.
6. Experience with Infrastructure as Code (Terraform, CDK, CloudFormation).
7. Good understanding of data architecture and distributed systems.
8. Experience implementing secure and compliant cloud solutions.

Preferred Qualifications

1. Experience with agentic AI frameworks and multi‑agent systems.
2. Familiarity with LLM orchestration tools (LangChain, LlamaIndex).
3. Experience with vector databases (Pinecone, OpenSearch, FAISS).
4. Knowledge of streaming and big data technologies (Kafka, Spark).
5. AWS certifications: AWS Certified Solutions Architect – Professional, AWS Machine Learning Specialty.
6. Experience in enterprise SaaS platforms or regulated industries.

Key Skills & Competencies

• Expertise in scalable AI system design.
• Strong 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 shape the organization’s AI platform strategy and execution, enabling rapid development and deployment of next‑generation AI solutions, including Generative 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.

Benefits

Relocation Assistance: No. Salary and benefits are competitive and aligned with the organization’s global scale, offering opportunities for career growth in a collaborative culture.