GenAI Manager

Industry/Sector

Not Applicable

Specialism

Platform Engineering & Architecture

Management Level

Manager

Job Description & Summary

The Opportunity

Join our Acceleration Center India and help shape the future of business for our diverse client portfolio across geographies and jurisdictions. You’ll work at the heart of global teams across Advisory, Assurance, Tax and Business Services—solving real client challenges through connected collaboration. We’ll help you grow your skills so you can go further. With hands-on learning, cutting-edge tools and an inclusive culture, this is your opportunity to do inspiring work that makes a difference—every day.

As a GenAI Manager you will lead the design and delivery of generative AI solutions within our Data and Analytics Engineering practice, focusing on data engineering work that supports data modernization and platform architecture needs. As a Manager you will lead teams, coach junior professionals, manage client accounts, and take ownership of planning, execution, and delivery across multiple workstreams while using sound judgment to navigate ambiguity and maintain quality. In this role at PwC Acceleration Center India, you will guide technical problem solving, help shape solution approaches, and support the development of scalable GenAI capabilities across cloud engineering and data modernization initiatives.

Responsibilities

- Leading GenAI and data modernization workstreams across cloud-based engineering engagements
- Managing project plans, budgets, and delivery timelines for data platform initiatives
- Guiding teams in developing GenAI solutions, data pipelines, and analytics-ready datasets
- Reviewing architecture, data modeling, and integration approaches for scalability and maintainability
- Coordinating the use of AWS, Azure, Snowflake, Databricks, and Airflow across client implementations
- Coaching junior team members on data engineering methods, platform tooling, and delivery processes
- Evaluating data quality, validation, security, and anonymization requirements for modern data environments
- Identifying opportunities to improve data infrastructure, automation, performance tuning, and query optimization
- Supporting client discussions on technology choices, delivery risks, and modernization roadmaps

What You Must Have

- At least a Bachelor's degree
- At least 8 years of experience
- Oral and written proficiency in English required

- Advanced programming skills in Python and fluency in data processing frameworks like Apache Spark. 

- Should have strong knowledge on LLM’s foundational model (OpenAI GPT4o, O1, Claude, Gemini etc), while need to have strong knowledge on opensource Model’s like Llama 3.2, Phi etc. 

- Proven track record with event-driven architectures and real-time data processing systems. 

- Familiarity with Azure DevOps and other LLMOps tools for operationalizing AI workflows. 

- Deep experience with Azure OpenAI Service and vector DBs, including API integrations, prompt engineering, and model fine-tuning. Or equivalent tech in AWS/GCP. 

- Knowledge of containerization technologies such as Kubernetes and Docker. 

- Comprehensive understanding of data lakes and strategies for data management. 

- Expertise in LLM frameworks including Langchain, Llama Index, and Semantic Kernel. 

- Proficiency in cloud computing platforms such as Azure or AWS. 

- Exceptional leadership, problem-solving, and analytical abilities. 

- Superior communication and collaboration skills, with experience managing high-performing teams. 

- Ability to operate effectively in a dynamic, fast-paced environment. 

What Sets You Apart

- Preference for at least one of the following fields of study: Management Information Systems, Computer and Information Science, Systems Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, Statistics, Mathematical Statistics
- Demonstrating experience leading GenAI data modernization workstreams
- Applying AI fluency across cloud engineering and analytics delivery
- Managing Databricks, Snowflake, or Azure Data Factory implementations
- Coaching teams through agile data engineering execution
- Translating business needs into data architecture decisions

Travel Requirements

Not Specified

Job Posting End Date