PWC - GenAI & Agentic AI Engineer

PwC; Hyderabad, Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

PwC seeks a hands-on GenAI engineer to build LLM-powered solutions inside real client environments. You will develop apps, agents, and RAG pipelines embedded within an enterprise client's team, moving quickly from prototype to production-ready solutions with AI tooling as a default.

Embedded in client projects, you will work across Python, TypeScript, and backend/frontend, collaborating with client teams via Teams, Slack, and email to deliver end-to-end AI capabilities.

Qualifications

• Hands-on GenAI/LLM development in production or near-production environments.
• Experience with AWS Bedrock and major LLM APIs (e.g., Anthropic, OpenAI).
• Practical use of at least one agent framework for orchestration.
• Capability to build RAG pipelines with embeddings and vector stores.

Responsibilities

• Build LLM-powered apps, agents, and automations for client projects.
• Design RAG pipelines using embeddings, vector search, and retrieval.
• Orchestrate multi-step workflows with agent frameworks.
• Write Python and some TypeScript across backend and light frontend.

Skills

GenAI/LLM work

AWS Bedrock

LLM APIs

Agent frameworks

RAG pipelines

Embeddings

Vector databases

Semantic search

Prompt engineering

Model evaluation

Python

TypeScript

APIs

Structured data

Unstructured data

Ambiguity tolerance

Curiosity

Tools

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

Docker

Kubernetes

CI/CD

Terraform

Databricks

Snowflake

Job description

Job Summary

A hands-on GenAI engineer who builds AI solutions inside real client environments. Embedded within an enterprise client's team, you will build LLM-powered apps, agents, and RAG pipelines that solve real problems, moving quickly from prototype to something the client can rely on, with AI tooling as your default way of working.

Key Responsibilities

• Build LLM-powered applications, agents, and automations that solve real client problems.
• Design and implement RAG pipelines using embeddings, vector search, and retrieval.
• Use agent frameworks to orchestrate multi-step workflows and tool use.
• Write clean Python (and some TypeScript) across backend and light frontend to ship end to end.
• Perform prompt engineering and set up evaluation to check the AI is doing its job.
• Integrate structured and unstructured enterprise data into solutions.
• Iterate quickly on feedback from real users.
• Use AI coding assistants and agent tooling as your default way of building.
• Collaborate with client teams over Teams, Slack, and email, and document your work.

Required Qualifications

• Software engineering experience with hands-on GenAI/LLM work in a production or near-production setting.
• Experience building with LLMs on AWS Bedrock and provider APIs (e.g., Anthropic Claude, OpenAI).
• Hands-on experience with at least one agent framework.
• Experience building RAG pipelines with embeddings, vector databases, and semantic search.
• Comfort with prompt engineering and basic model evaluation.
• Strong Python (and ideally some TypeScript), comfortable across backend and light frontend, building APIs.
• Good data fundamentals across structured and unstructured data.
• Comfort with ambiguity and a bias to build and learn.

Preferred Qualifications

• AWS AI services (Bedrock, SageMaker).
• Enterprise AI platforms (Palantir Foundry/AIP, Databricks, Snowflake Cortex).
• Production deployment (Docker, Kubernetes, CI/CD, Terraform).
• Experience in regulated industries.
• Prior consulting, customer success, or forward-deployed experience.
• AWS Certified Solutions Architect Associate; an AI/ML certification such as AWS Certified Machine Learning Specialty.

Technical Skills & Tools

• LLMs & AI: AWS Bedrock, Anthropic Claude, OpenAI; prompt engineering
• Agent frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen
• Retrieval & RAG: embeddings, vector databases (Pinecone, pgvector, Weaviate, Qdrant, OpenSearch), semantic search
• Evaluation & observability: model evaluation, Langfuse, OpenTelemetry
• Languages: Python, TypeScript / JavaScript
• Backend & APIs: FastAPI, Node.js, REST, GraphQL
• Frontend: React, TypeScript
• Data: SQL / NoSQL, data pipelines, structured and unstructured data
• Good to have: AWS SageMaker, Docker, Kubernetes, CI/CD, Terraform, Databricks, Snowflake

Soft Skills & Competencies

• Comfort with ambiguity and a bias to build and learn.
• Clear communication with technical and non-technical audiences.
• Takes ownership of outcomes.
• Collaborative and fast-moving.

Experience Required

2-5 years.

Reporting & Team

Embedded within an enterprise client's team as part of our Forward Deployed Engineering practice, working alongside the client's engineers and our senior engineers.

Location & Work Model

• Locations: Bengaluru or Hyderabad.
• Work model: Forward-deployed and customer-facing; embedded within an enterprise client's team.
• Working hours: Overlap with client business hours (including US / EST); works at the client's delivery pace.