Industry/Sector
Not Applicable
Specialism
Data, Analytics & AI
Management Level
Manager
Job Description & Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.
Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven decision making. You will work on developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems.
Enhancing your leadership style, you motivate, develop and inspire others to deliver quality. You are responsible for coaching, leveraging team member’s unique strengths, and managing performance to deliver on client expectations. With your growing knowledge of how business works, you play an important role in identifying opportunities that contribute to the success of our Firm. You are expected to lead with integrity and authenticity, articulating our purpose and values in a meaningful way. You embrace technology and innovation to enhance your delivery and encourage others to do the same.
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
- Analyse and identify the linkages and interactions between the component parts of an entire system.
- Take ownership of projects, ensuring their successful planning, budgeting, execution, and completion.
- Partner with team leadership to ensure collective ownership of quality, timelines, and deliverables.
- Develop skills outside your comfort zone, and encourage others to do the same.
- Effectively mentor others.
- Use the review of work as an opportunity to deepen the expertise of team members.
- Address conflicts or issues, engaging in difficult conversations with clients, team members and other stakeholders, escalating where appropriate.
Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.
The Opportunity
- When you join PwC Acceleration Centers (ACs), you step into a pivotal role focused on actively supporting various Acceleration Center services, from Advisory to Assurance, Tax and Business Services. In our innovative hubs, you’ll engage in challenging projects and provide distinctive services to support client engagements through enhanced quality and innovation. You’ll also participate in dynamic and digitally enabled training that is designed to grow your technical and professional skills.
- As part of the Data and Analytics team you will design and deploy innovative Generative AI solutions that transform business operations. As a Manager you will lead cross-functional teams, mentor junior staff, and drive the execution of our AI strategy, making sure alignment with ethical practices and operational excellence.
Responsibilities
- Architect end-to-end enterprise GenAI solutions focusing on agentic system designs, including multi-agent orchestration, autonomous task execution, tool-use chains, LLM integration, and RAG pipelines.
- Define and enforce agentic design standards across teams covering agent communication protocols, intelligent task routing, lifecycle management, shared tool registries, and orchestration framework selection (e.g., LangGraph, CrewAI, AutoGen, Semantic Kernel Agents, cloud-based frameworks).
- Align technology choices with clients’ preferred cloud platforms and infrastructure, ensuring compliance, cost efficiency, portability, and seamless integration with existing governance policies and services.
- Collaborate with business stakeholders to translate objectives into agentic AI requirements, defining agent capabilities, success metrics, and MVP scope for iterative delivery.
- Establish robust observability, testing, and operational standards for agentic AI solutions including telemetry, monitoring, automated regression tests, simulation environments, and production SLAs to ensure reliability.
- Lead and mentor GenAI/Agentic AI engineering teams through architecture reviews, code-level guidance, sprint planning, and quality assurance to ensure high-quality solution delivery.
- Evaluate, select, and implement agentic AI technology stacks encompassing vector databases (Pinecone, Weaviate, Azure AI Search), orchestration frameworks, observability tools (LangSmith, Langfuse, Arize AI), and AI-powered development tools, complemented by strong expertise in DevOps, LLMOps, containerized architectures, and CI/CD pipelines (Azure DevOps, GitHub Actions).
What You Must Have
- Bachelor's & Master's Degree in computer science, Data Science, or a related field
- 8+ years of experience
- Oral and written proficiency in English required
What Sets You Apart
- Over 3 years of experience in developing and scaling Generative AI projects from prototypes to enterprise production, managing throughput, latency, cost, and multi-region deployments.
- Proven expertise implementing AI interoperability protocols like MCP (Model Context Protocol) and A2A (Agent-to
- Agent) at scale for seamless system integration.
- Skilled in using enterprise cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, and Google Vertex AI to build and deploy production-grade agentic AI solutions.
- Advanced Python programming skills and hands-on experience with agentic AI frameworks including LangChain, LangGraph, CrewAI, and AutoGen for building robust generative AI applications.
- Deep understanding of advanced Retrieval
- Augmented Generation (RAG) architectures (Graph RAG, Vectorless RAG, Hybrid RAG) and traditional AI/ML fundamentals like model building, fine-tuning, and evaluation.
- Strong knowledge of LLM security risks—prompt injection, jailbreaking, data exfiltration, tool misuse—and experience designing defense-in-depth safeguards within agentic system architectures.
- Expertise in containerization and cloud-native orchestration (Kubernetes, Docker, serverless) and event-driven architectures for scalable deployment of agentic AI workloads; holds relevant AI or solution architecture certifications.
Travel Requirements
0%
Job Posting End Date
