Associate GenAI

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 intelligent automation at PwC will focus on conducting process mining, designing next generation small- and large-scale automation solutions, and implementing intelligent process automation, robotic process automation and digital workflow solutions to help clients achieve operational efficiencies and reduce costs.

Job Summary:

We are seeking a passionate and skilled AI Engineer with 3-7 years of experience, including a minimum of 2 years in Generative AI (GenAI), to join our AI/ML team. The AI Engineer will play a key role in building and deploying advanced AI solutions leveraging Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), multi-agent frameworks, and state-of-the-art tooling.

Responsibilities:

• Design, develop, and deploy GenAI solutions, with a focus on real-world production readiness.
• Build and scale Retrieval-Augmented Generation (RAG) systems for enterprise use cases.
• Integrate with graph-based memory and tool-using agents to enhance LLM capabilities.
• Implement multi-agent orchestration and reasoning systems (e.g., AutoGen, LangGraph, custom agents).
• Use LLM frameworks and libraries (e.g., LangChain, AutoGen, LangGraph, etc.).
• Write clean, efficient, and scalable code in Python (or other languages as needed).
• Collaborate across cross-functional teams to translate business needs into scalable GenAI-powered systems.
• Ensure system scalability, reliability, and maintainability through strong software engineering practices.

Mandatory skill sets:

• 3-7 years of total experience in AI/ML or software engineering.
• Minimum 2 years of hands-on experience in building and deploying GenAI systems.
• Experience in working with LLMs such as GPT-4, Claude 2/Gemini, etc.
• Strong hands-on knowledge of RAG systems and multi-agent frameworks like LangGraph, LangChain, or AutoGen.
• Proficiency in Python and familiarity with cloud-native deployments (e.g., APIs, containers, microservices).
• Solid understanding of software engineering best practices, including version control, testing, and CI/CD.
• Experience in working with graph-based memory and LLM tool use patterns.

Preferred skill sets:

• Experience with tool-using agents, memory-augmented architectures, or cognitive architectures.
• Prior contributions to open-source GenAI or agent frameworks.
• Familiarity with Azure OpenAI, AWS Bedrock, or other cloud-based LLM services.
• Background in optimization, evaluation, and prompt engineering for LLM systems.
• Experience in deploying AI systems in production environments and maintaining performance at scale.

Education qualification:

Bachelor's degree in Computer Science, Engineering, or a related field.

Education (if blank, degree and/or field of study not specified):

Degrees/Field of Study required: Master of Engineering, MBA (Master of Business Administration), Bachelor of Engineering

Degrees/Field of Study preferred:

Required Skills:

Agentic AI

Optional Skills:

Accepting Feedback, Active Listening, Agile Methodology, AI Fluency, AI-Human Collaboration, Automation Algorithms, Automation Engineering, Automation Framework Design and Development, Automation Programming, Automation Solutions, Automation Studio, Automation System Efficiency, Blue Prism, Business Analysis, Business Performance Management, Business Process Analysis, Business Process Automation (BPA), Business Transformation, Business Value Optimization, C++ Programming Language, Cognitive Automation, Communication, Conducting Discovery, Configuration Management (CM) {+ 34 more}