AI Solutions Architect Manager

About the Role

At PwC, our people in risk and compliance focus on maintaining regulatory compliance and managing risks for clients, providing advice, and solutions. They help organisations navigate complex regulatory landscapes and enhance their internal controls to mitigate risks effectively. In regulatory risk compliance at PwC, you will focus on confirming adherence to regulatory requirements and mitigating risks for clients. You will provide guidance on compliance strategies and help clients navigate complex regulatory landscapes.

Responsibilities

• Deep hands-on expertise in AWS cloud architecture, including multi-account design, VPC architecture, IAM strategy, networking, storage, compute, and high-availability deployment models.
• Design and deploy production-grade Generative AI and Agent-based systems on AWS using services such as Amazon Bedrock, SageMaker, ECS/EKS, Lambda, S3, OpenSearch, API Gateway, and related services.
• Architect end-to-end AI systems including: Retrieval-Augmented Generation (RAG) pipelines, Vector database integrations, Embedding pipelines, Agent orchestration frameworks, API/MCP-driven tool integrations, and AI services.
• Own infrastructure sizing, capacity planning, scalability modeling, and performance optimization for AI workloads (compute, GPU/CPU allocation, autoscaling, concurrency management).
• Design cloud-native AI architectures optimized for reliability, cost efficiency (FinOps alignment), and operational scalability.
• Implement secure AI deployment patterns including IAM least privilege and agency, encryption at rest and in transit, secrets management, network segmentation, logging, and monitoring.
• Build and operationalize MLOps / LLMOps pipelines including: Model deployment automation, CI/CD integration, Versioning and rollback strategies, Monitoring and drift detection.
• Design resilient AI systems incorporating failover strategies, backup mechanisms, disaster recovery planning, and high-availability patterns.
• Identify and mitigate AI-specific technical risks including prompt injection, model misuse, data leakage, performance degradation, and adversarial manipulation.
• Integrate AI solutions with enterprise systems including APIs, SaaS platforms, data lakes, security tools, and governance platforms.
• Provide technical leadership during build and deployment phases, conducting architecture reviews, performance tuning, and production readiness validation.
• Strong hands-on proficiency in Python and AI frameworks to prototype, validate, and troubleshoot deployed AI solutions.
• Experience deploying containerized AI applications using Docker, Kubernetes (EKS), or serverless architectures.
• Ability to troubleshoot production issues across infrastructure, application, and model layers.
• Balance architecture decisions across security, scalability, performance, cost, and operational complexity.

Desired Knowledge

• Strong hands-on experience architecting and deploying AI solutions in AWS production environments (not limited to conceptual or advisory roles).
• Deep understanding of LLM deployment patterns, model hosting strategies (managed vs custom), inference optimization, and API integration models.
• Experience with agent frameworks and orchestration layers for multi-step reasoning and workflow automation.
• Strong understanding of AWS networking, load balancing, autoscaling, container orchestration, and infrastructure-as-code (Terraform / CloudFormation).
• Familiarity with GPU-based workloads, model performance tuning, and concurrency management.
• Experience implementing monitoring and observability for AI systems (CloudWatch, logging frameworks, performance metrics).
• Knowledge of cost optimization strategies for AI workloads in cloud environments.
• Understanding of AI security considerations and cloud threat modeling for deployed AI systems.
• Maintain and advise on development and architecture standards and lead practices.
• Mentor junior AI developers through PwC’s apprenticeship model.

Professional & Educational Background

• BE / B Tech / MCA / MS (Field of Study: Computer Science, AI/ML, Information Technology, or related fields.
• 10+ years of overall IT experience, with at least 6+ years in AI/ML, cloud solution architecture, or enterprise platform engineering roles.
• 6+ years leading enterprise engagements or managing cross-functional technical teams, with demonstrated ownership of large-scale AI or cloud transformation initiatives.
• Proven experience architecting and scaling secure, production-grade AI and cloud solutions within enterprise or regulated environments.
• Strong hands-on AWS architecture experience is mandatory.
• Certification(s) Preferred: AWS Solutions Architect (Associate/Professional), AWS Machine Learning Specialty, Azure AI Engineer Associate, Certified Information Systems Security Professional (CISSP), Certified Information Systems Manager (CISM), Certified Information Systems Auditor (CISA), or Certified in Risk and Information Systems Control (CRISC).

Key skills/competency

• AWS Cloud Architecture
• Generative AI Deployment
• LLMOps
• Python
• Containerization (Docker, EKS)
• Infrastructure as Code (Terraform, CloudFormation)
• AI Security
• Performance Optimization
• MLOps
• Risk Management