AI Solution Architect

Role Summary:

AI Solution Architect

solution design and rapid prototyping

Key Responsibilities:

• Solution Design:

  Lead the design of

  end-to-end AI and GenAI architectures

  , including model selection, orchestration frameworks, data integration, and security patterns.

• Prototyping:

  Build and demonstrate

  proof-of-concept AI/GenAI prototypes

  using modern tools (e.g., OpenAI, LangChain, LlamaIndex, CrewAI, Anthropic, vector databases, ReactJS).

• Advisory:

  Partner with clients and delivery teams to define

  AI transformation strategies

  , roadmap development, and operating models aligned with PwC’s Responsible AI framework. Look for opportunities to embed AI in digital platforms/solutions. Look at the broader picture for implementing AI in planned workflows as well as challenge the status-quo on existing solution structuring.

• Architecture Leadership:

  Develop

  reference architectures

  and reusable

  solution accelerators

  for LLM-based systems, agentic workflows, and multi-agent orchestration.

• Cross-functional Collaboration:

  Work with

  data, cloud, and industry experts

  to design AI solutions that integrate seamlessly into enterprise ecosystems.

• Innovation Enablement:

  Stay ahead of evolving AI technologies (agentic systems, autonomous reasoning, multimodal models) and bring thought leadership into client conversations.

Required Skills & Experience:

• 8+ years of experience in

  technology architecture, AI/ML systems, or enterprise solution design

  , ideally within consulting or large-scale digital transformation programs.
• Deep understanding of

  Generative AI models

  ,

  LLM orchestration

  , and

  agentic AI frameworks

  .
• Hands-on familiarity with

  cloud-native architectures (AWS, Azure, GCP)

  and modern AI development tools.
• Experience with

  Python

  ,

  APIs

  ,

  prompt engineering

  ,

  RAG (Retrieval-Augmented Generation)

  , and

  vector search systems

  .
• Strong ability to

  bridge business and technology

  , creating value-driven, scalable AI solutions.
• Excellent stakeholder management, communication, and presentation skills—comfortable working with executive and technical audiences.

Preferred Qualifications:

• Prior experience designing or piloting

  autonomous or agent-based AI systems

  .
• Knowledge of

  AI governance

  ,

  model risk

  , and

  responsible AI practices

  .
• Certifications in

  cloud architecture

  ,

  AI/ML

  , or

  data engineering

  .
• Consulting experience in AI strategy, architecture design, or digital transformation.