Artificial Intelligence Engineer

PriceWaterhouseCoopers Pvt Ltd ( PWC ); Mumbai City; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

PwC seeks a hands-on AI Engineer to design, build, and scale production-grade AI applications driven by LLMs and agentic workflows. You will work with Azure services, MLOps, and security teams to deliver secure, observable AI features for enterprise apps.

The role emphasizes AI/ML engineering, backend development, and production readiness, with strong emphasis on governance and data protection.

Qualifications

• Experience building production AI integrations using LLMs, agents, orchestration frameworks, and APIs.
• Hands-on experience with Agentic AI, A2A frameworks, MCP Protocol, tool 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.

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.

Skills

LLM integrations

Agentic AI

A2A frameworks

LangChain

LangGraph

Python

Java or Node.js

Azure Cloud

Azure Functions

Education

Bachelor's in Engineering

Tools

Azure Functions

Azure Container Apps

Redis

Cosmos DB

Azure AI Search

LangChain

LangGraph

Job description

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 artificial intelligence engineering at PwC will apply data, algorithms, and software engineering to build and deploy software and platform systems that create Artificial Intelligence and Machine Learning based solutions at scale. Your work will involve designing AI systems, data wrangling, and software implementation to enable the AI models to be useful and scalable.

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.

Job Description & Summary

We are seeking a hands-on AI Engineer to design, build, integrate, and support production-grade AI applications powered by LLMs, agentic workflows, RAG, vector search, AI Gateway integrations, and cloud-native Azure services. The role requires strong experience in AI/ML engineering, backend development, prompt and context engineering, orchestration frameworks, and production readiness practices. The engineer will work closely with products, platform, MLOps, security, compliance, and application engineering teams to deliver secure, scalable, observable, and high-quality AI features for enterprise applications.

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.

Years of experience required: 4-7 Years Education Qualification: B.Tech Degrees/Field of Study required: Bachelor of EngineeringDegrees/Field of Study preferred: Certifications (if blank, certifications not specified)

Required Skills Data Analyzer, Data Center Engineering Optional Skills Accepting Feedback, Accepting Feedback, Active Listening, AI Fluency, AI-Human Collaboration, AI Implementation, C++ Programming Language, Communication, Complex Data Analysis, Data Analysis, Data Infrastructure, Data Integration, Data Modeling, Data Pipeline, Data Quality, Deep Learning, Digital Tooling, Emotional Regulation, Empathy, GPU Programming, Inclusion, Intellectual Curiosity, Java (Programming Language), Machine Learning, Machine Learning Libraries {+ 23 more} Desired Languages (If blank, desired languages not specified)

Travel Requirements Available for Work Visa Sponsorship? Government Clearance Required?

Job Posting End Date October 13, 2026