AI Engineer

At PwC, our people in legal services offer comprehensive legal solutions and advice to internal stakeholders and clients, maintaining compliance with regulations and minimising legal risks. These individuals provide strategic guidance and support across various industries. In privacy law and data protection at PwC, you will specialise in providing advice and guidance to clients on privacy laws and data protection regulations. You will help businesses navigate the complex landscape of privacy and data protection requirements, confirming compliance with applicable laws and regulations. Working in this area, you will assist in developing privacy policies, conducting privacy impact assessments, and implementing data protection measures to safeguard personal information.

Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow.

Skills

Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:

• Respond effectively to the diverse perspectives, needs, and feelings of others.
• Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems.
• Use critical thinking to break down complex concepts.
• Understand the broader objectives of your project or role and how your work fits into the overall strategy.
• Develop a deeper understanding of the business context and how it is changing.
• Use reflection to develop self awareness, enhance strengths and address development areas.
• Interpret data to inform insights and recommendations.
• 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.
  
  

We are looking for a skilled AI Solution Developer to design and deploy modular AI solutions as part of our “AI-in-a-Box” initiative. This role combines hands-on AI/ML development, solution architecture, and end-to-end lifecycle management. You’ll work with advanced AI technologies such as LLMs, RAG pipelines, microservices, Vector Database and Knowledge Graph to build deployable solutions (Activate, Deactivate, Remove) from the Client environment with ease.

Key Responsibilities

• Design and build modular AI solutions using LangChain, Semantic Kernel, or custom pipelines.
• Develop APIs and AI agents for use cases like summarization, Q&A, and chatbots.
• Work with business teams to translate requirements into technical solutions.
• Build data pipelines to ingest content from platforms like SharePoint and databases.
• Containerize solutions and deploy them across cloud environments (Azure AKS, App Service) using Docker and CI/CD pipelines.
• Track performance metrics, logs, and telemetry continuously improve retrieval quality, and latency etc.
  
  

Required Skills

• Strong Python skills and experience with AI libraries (LangChain, Transformers, OpenAI SDK, LLaMA/LLM APIs).
• Experience with RAG pipelines, knowledge of embedding models and Prompt Design.
• Familiarity with Knowledge Graphs (Apache Jena, SPARQL) and KG-based retrieval in LLM pipelines.
• Hands-on with Vector Database like Pinecone, Chroma or similar.
• Ability to develop and deploy microservices (FastAPI, Flask).
• Cloud deployment skills with Azure (AKS, App Service, ACI) and DevOps tools (GitHub Actions, Azure Pipelines).
• Experience integrating with enterprise tools like Outlook, Teams, and Salesforce.
  
  

Preferred Experience

• Delivery of at least 2 AI projects (PoC or production).
• Strong collaboration with business and technical stakeholders.
• Knowledge of Responsible AI practices, including evaluation and testing
• Familiarity with packaging and managing AI lifecycles.