GenAI Architect at PricewaterhouseCoopers (PwC)

Qualifications / Certifications required:

• Bachelor’s Degree in Mathematics/Statistics and 8 or more years of experience in the Predictive Analytics area 
• Degree or Certification in AI and GenAI from reputed university

Experience required:

• Proven experience as an Enterprise Architect, Solution Architect, or a similar role, with a focus on AI/ML and generative AI.  
• Proven architecture experience within the Retail Banking sector
• Strong understanding of generative AI techniques, including deep generative models, autoregressive models, and reinforcement learning for generative tasks.  
• Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, ML and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and ML techniques.  
• Proven expertise with LLMs (Microsoft Azure Open AI – Chat GPT 4.0 and other LLM models) including API access for GenAI.  
• Proven expertise in CI/CD pipeline design and management in the Generative AI space including experience with LLMOPS.  
• Understanding of AI agents (both worker and orchestrator) to facilitate decisioning and deployment of complex applications.
• Proven experience with Python development and troubleshooting code.
• Strong knowledge of containerization and orchestration tools.
• Experience with cloud platforms (AWS, Azure) and infrastructure as code.
• Multi-year experience with AI and machine learning concepts and tools.
• Exceptional communication and collaboration skills. 

Responsibilities of role:

• Understand client's vision, goals, and strategies. Aligns department priorities appropriately, determines critical success factors, evaluates, and pursues initiatives based on fit with strategies.  
• Play an instrumental and influential role in driving Generative AI vision, strategy, and architecture.  
• Designing, developing, and overseeing the implementation of GenAI solutions that solve complex business problems. You'll work closely with product managers, software engineers, data scientists, and other customers to conceptualize, prototype, test, and release GenAI Solutions.
• Design and develop the architecture and infrastructure for GenAI systems, including data storage, processing, and retrieval mechanisms. Ensure scalability, flexibility, performance, and efficiency of the GenAI systems to handle large volumes of data and complex algorithms seamlessly.
• Keep current on latest developments in the field of AI, Machine Learning, Deep Learning, NLP, Image processing and by reading relevant papers/ journals and applying the new learnings to create innovative data and analytics solutions.
• Conduct POCs, Pilots on reusable, scalable data processing, curation, and consumption frameworks to eliminate point solutions.
• Take ownership of operationalization of AI processes while designing solutions to optimize data pipelines, model deployment, and model monitoring.
• Evaluate, analyze, and optimize system resources related to large scale data processing, image processing, computer vision and deep learningDesign and build scalable infrastructure and platform to ingest, store and process very large amounts of data (structured, semi-structured and unstructured), including streaming and real-time into the cloud-based Data Lake
• Work closely with the engineering and the platform team to deliver technical solutions. This includes end to end data orchestration, data layering, data governance, metadata management and data cataloging.
• Partner with data science, actuarial science, underwriting, claims and various business groups on deep rooted data analysis including propensity scores, catastrophic modeling, time-series analysis, and forecasting.
• Should lead the decision-making process for technical and architectural choices with PwC alliance partners (for example: Microsoft, SAP, Amazon etc.) to ensure that solutions are scalable, maintainable, and cost-effective.
• Ensure alignment to ethical AI practices, such as fairness, transparency, and accountability. Address biases and potential risks associated with AI systems to ensure responsible deployment and usage.
• Test and evaluate different Gen AI models and algorithms to determine their effectiveness to meet business needs. Should Provide technical guidance and mentorship to Development and other team members.
• Define AI solution objectives and ensure alignment with business outcomes
• Monitor AI industry trends and maintain state-of-the-art industry knowledge
• Implement best practices for AI testing, deployment, and maintenance