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Gen AI Architect- Python, Google ADK - Manager

PwC Acceleration Centres · Bangalore, India

About this role

The Opportunity: Join the Acceleration Center India to design and deliver data and analytics engineering solutions within the Data, Analytics & AI practice. You will lead client-facing workstreams, focusing on GenAI architecture, Python development, and Google ADK-based implementations. As a Manager, you will guide teams, manage project delivery, and shape technical architecture decisions. Responsibilities: Lead client-facing Gen AI architecture workstreams; design and build scalable data and AI solutions including pipelines and integration layers; translate business requirements into technical architecture and implementation plans; guide teams in selecting data engineering tools and orchestration frameworks like Apache Airflow and Databricks; oversee data modeling and quality controls; review technical deliverables for security and architecture alignment; coach team members on coding patterns and performance tuning; manage project scope, timelines, and stakeholder expectations; and apply data governance and privacy practices. Required Qualifications: Bachelor's degree; at least 8 years of experience; oral and written proficiency in English. Preferred Qualifications: Degree in Management Information Systems, Computer Science, Systems Engineering, Electrical/Chemical/Industrial Engineering, Mathematics, or Statistics; experience building GenAI solutions with Python and Google ADK; experience leading client-facing data and AI engineering workstreams; project management experience including budgeting and execution; experience with AWS, Databricks, or Snowflake.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AIPythonGoogle ADKData EngineeringData ArchitectureApache AirflowDatabricksSnowflakeAWSData GovernanceProject Management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Client-Facing GenAI Architecture Leadership
The role requires leading workstreams that translate complex business needs into scalable technical architectures using Python and Google ADK. Success depends on your ability to bridge the gap between high-level client requirements and concrete implementation plans while maintaining architectural standards.
Technical Oversight and Team Mentorship
As a Manager, you must oversee the development of data pipelines, integration layers, and AI solutions. You are expected to coach team members on coding patterns, performance tuning, and delivery methods, ensuring that all technical deliverables align with security and data governance requirements.
Project Delivery and Stakeholder Management
You are responsible for managing project scope, timelines, and risks across multiple client engagements. This involves balancing technical decision-making with the practical constraints of project budgeting and execution to meet client expectations.

Likely challenges to think through

Balancing Rapid GenAI Innovation with Governance
Implementing GenAI solutions often conflicts with strict data privacy and governance standards. You may face difficulty in designing architectures that are both innovative and compliant with enterprise-grade security, requiring you to demonstrate deep knowledge of anonymization and privacy practices.
Managing Multi-Stream Delivery Complexity
Leading multiple client-facing workstreams simultaneously creates a risk of scope creep and resource misalignment. To prepare, focus on your methodology for prioritizing tasks, managing stakeholder expectations, and maintaining consistent quality across diverse technical teams.

Practice your answers

Technical practice

GenAI Architecture Patterns

What to coverExplain how you design scalable GenAI architectures. Focus on how you integrate LLMs with existing data pipelines, manage context windows, and ensure low-latency retrieval-augmented generation (RAG) performance.

Google ADK Implementation

What to coverDescribe your experience using Google ADK for AI solutions. Discuss how you leverage these tools to streamline development and the specific challenges you have encountered when integrating them into enterprise environments.

Data Engineering Orchestration

What to coverCompare the use of Apache Airflow versus other orchestration tools in a large-scale data project. Explain how you determine the right framework for complex dependency management and pipeline reliability.

Data Governance and Privacy in AI

What to coverHow do you incorporate data governance and privacy, such as PII anonymization, into the design of AI solutions? Discuss the trade-offs between model performance and strict data compliance requirements.

Scalable Data Modeling

What to coverDetail your approach to designing data models that support both traditional analytics and GenAI workloads. Address how you ensure data quality and consistency across integration layers.

Cloud Platform Trade-offs

What to coverWhen choosing between Databricks, Snowflake, or AWS for a new AI project, what criteria do you prioritize? Suggestion: Prepare to discuss cost, scalability, and native AI/ML feature support for each platform.

Performance Tuning in Python

What to coverWhat strategies do you use to coach teams on Python performance tuning for data-intensive applications? Focus on memory management, efficient library usage, and parallel processing techniques.

Translating Business Requirements

What to coverDescribe your process for translating high-level business needs into a concrete technical architecture. Explain how you balance technical debt with the need for rapid delivery in client-facing roles.

Behavioural practice

Managing Conflicting Stakeholder Priorities

What to coverDescribe a situation where you had to manage competing expectations from multiple stakeholders on a project. Explain how you communicated the technical risks and arrived at a consensus.

Mentoring and Team Development

What to coverProvide an example of how you have mentored a team member to improve their technical output or coding standards. Focus on the specific feedback you provided and the resulting impact on the project.

Navigating Delivery Risks

What to coverTell me about a time a project was at risk of missing a deadline due to technical challenges. How did you identify the root cause, adjust the plan, and ensure successful delivery?

Leading Client-Facing Workstreams

What to coverDescribe a challenging client interaction where you had to explain a complex technical architecture decision. How did you ensure the client understood the value and aligned with your recommendation?

Sharpen your knowledge

Topics to prepare

Generative AI Architecture Patterns
Focus on designing scalable AI solutions that integrate LLMs into existing data pipelines. Exercise: Draft a high-level architecture diagram for a RAG (Retrieval-Augmented Generation) system, detailing how data flows from source to integration layers and how you would ensure data quality and security at each stage.
Data Governance and Privacy in AI
Understand the application of privacy and anonymization practices within AI workflows. Exercise: Review a hypothetical data pipeline and identify potential PII (Personally Identifiable Information) leakage points, then propose specific governance controls or anonymization techniques to mitigate these risks.
Client-Facing Project Management
Focus on balancing technical delivery with stakeholder expectations and project scope. Exercise: Create a mock project plan for a 3-month GenAI implementation, identifying key milestones, potential risks, and a strategy for communicating technical constraints to non-technical stakeholders.

Skills to revise

Python for AI Engineering
Demonstrate proficiency in coding patterns and performance tuning for data-intensive applications. Exercise: Refactor a sample Python script that processes large datasets to improve execution efficiency and modularity, focusing on clean code practices suitable for team environments.
Orchestration with Apache Airflow
Master the design of complex data workflows. Exercise: Design a Directed Acyclic Graph (DAG) in Airflow that handles data ingestion, transformation, and a downstream AI model inference step, including error handling and retry logic.
Cloud Data Platform Implementation
Apply knowledge of Databricks, Snowflake, or AWS to AI solutions. Exercise: Compare the architectural advantages of using Databricks versus Snowflake for a specific GenAI use case, justifying your choice based on scalability, integration, and cost-efficiency.

Your preparation checklist

Prepare a GenAI Architecture Case Study
Draft a structured walkthrough of a past project where you designed a scalable AI solution using Python. Focus on how you translated business requirements into technical architecture, specifically highlighting your experience with Google ADK or similar frameworks and how you managed data pipelines and integration layers.
Document Your Leadership and Mentorship Approach
Prepare specific examples of how you have coached team members on coding patterns, performance tuning, and technical delivery. Be ready to discuss your methodology for reviewing technical deliverables to ensure they meet security, architecture, and quality standards.
Synthesize Data Governance and Engineering Expertise
Create a summary of your experience applying data governance, privacy, and anonymization practices within AI projects. Include your familiarity with orchestration tools like Apache Airflow and cloud platforms such as Databricks, AWS, or Snowflake to demonstrate your ability to oversee complex data ecosystems.
Formulate Strategic Questions for the Interviewer
Prepare thoughtful questions regarding the team's current approach to client-facing GenAI workstreams, such as how they balance project scope and timelines with evolving AI security requirements, or how the firm integrates Google ADK into their broader data engineering strategy.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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