IN_Senior Associate_AI Engineer_Data and Analytics_Advisory_Bangalore

*Why PWC

At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us.

At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. "

Responsibilities: 

• Build application-facing AI features powered by LLMs, agentic workflows, and AI orchestration platforms. 

• Integrate with MLOps-managed agents and consume LLM capabilities through AI Gateway patterns including routing, authentication, policy controls, and logging. 

• Design and implement agentic workflows using tool calling, function calling, context handling, prompt/version management, and orchestration patterns. 

• Work with Agentic Layer A2A frameworks and MCP Protocol to enable interoperable agent communication and workflow execution. 

• Develop RAG-based solutions using vector embeddings, vector databases, Azure AI Search, Redis, Cosmos DB, and relevant cloud data stores. 

• Build and orchestrate LLM-powered applications using LangChain, LangGraph, and related frameworks. 

• Deploy scalable AI solutions on Azure using Azure Functions, Azure Container Apps, and cloud-native architecture patterns. 

• Optimize AI applications for latency, scalability, reliability, cost, and performance. 

• Implement production readiness practices including testing, observability, monitoring, evaluations, guardrails, safety controls, and incident support. 

• Partner with security, compliance, architecture, and platform teams to meet governance, data protection, and enterprise control requirements. 

• Document integration patterns, architecture, operational runbooks, evaluation practices, and reusable engineering components. 

Mandatory Skill Sets: 

• Experience building production AI integrations using LLMs, agents, orchestration frameworks, and APIs. 

• Hands-on experience with Agentic AI, A2A frameworks, MCP Protocol, tool/function calling, and workflow orchestration. 

• Strong expertise in vector embeddings, prompt engineering, prompt versioning, context engineering, and RAG. 

• Experience with LangChain and LangGraph. 

• Strong programming skills in Python and at least one backend language such as Java or Node.js. 

• Proficiency in Azure Cloud deployment. 

• Experience with Azure AI Search, vector databases, Redis, Cosmos DB, and related data platforms. 

• Proven ability to design and manage Azure Functions and Azure Container Apps. 

• Strong software engineering fundamentals including testing, observability, reliability, secure integration, and production support. 

• Familiarity with evaluation methods, guardrails, monitoring, and safety/quality controls. 

• Strong understanding of cloud-native architecture, scalability, and performance optimization. 

• Strong interpersonal / good negotiations skills are required. 

Preferred Skill Sets: 

• Experience with Blob Storage, Iceberg, Kubernetes, Docker, CI/CD, and MLOps pipelines. 

• Exposure to Azure AI Foundry, Azure OpenAI, AWS AI services, or multi-cloud AI platforms. 

• Experience with enterprise AI Gateway patterns, policy enforcement, and GenAI governance. 

• Experience building reusable AI starter kits, orchestration templates, evaluation harnesses, or observability frameworks. 

• Knowledge of responsible AI, data privacy, compliance, and secure enterprise AI delivery.  

4-8 Yrs

B.E, B.Tech, M.E, MCA, M.Tech