Sr Associate_AI/Agentic Platform_GCC_Advisory

Job 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 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 Description Summary

We are seeking an experienced Agentic Platform Engineer with strong hyperscaler cloud engineering skills and deep Python expertise to design, develop, and deploy intelligent agentic systems across PGCs multi-cloud, multi-platform estate. Unlike a single-cloud specialist role, this position demands genuine platform agnosticism - you will build agents that span Azure AI, Oracle Agentic Studio, and open-source Python frameworks, integrating with Oracle ERP/HCM, Pega, and ServiceNow as PGC transitions from UiPath RPA to next-generation autonomous agent architectures.

Responsibilities

• Design and architect scalable, cloud-native solutions on Azure (primary) and AWS (secondary).
• Implement microservices and serverless architectures: Azure Functions / AWS Lambda, API Gateway, Container Instances.
• Deploy and manage containerized workloads using Docker, Kubernetes (AKS / EKS), and Helm.
• Design event-driven, async architectures using cloud messaging services (Azure Event Hub, AWS SQS/SNS, Apache Kafka).
• Implement IaC using Terraform (primary) or CloudFormation/ARM for repeatable, auditable deployments.
• Design and build intelligent agentic systems that automate multi-step workflows and autonomous decision-making.
• Architect and implement multi-agent systems: task decomposition, specialist sub-agents, orchestrator/supervisor patterns, swarm coordination.
• Implement RAG (Retrieval-Augmented Generation) pipelines: document ingestion, chunking, embedding, vector retrieval, re-ranking, response synthesis.
• Integrate and abstract multiple LLM providers: Azure OpenAI, OpenAI, Anthropic Claude, in a provider-agnostic pattern.
• Design structured output and function/tool calling patterns for reliable agent-to-system interactions.
• Build agent memory architectures: short-term (in-context), long-term (vector store / relational), episodic (session history).
• Evaluate agent and LLM quality: implement RAGAS, LangSmith, PromptFlow, or custom evaluation harnesses in CI/CD.
• Apply prompt engineering best practices: chain-of-thought, structured prompting, output constraints, guardrails.
• Implement agentic workflows using LangChain, AutoGen, CrewAI, or similar - with a view to platform-specific integration (Oracle Agentic Studio, Semantic Kernel).

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.