Gen AI & Agentic AI Engineer

PwC Acceleration Center India; Hyderabad; On-site; INR 2,500,000 - 4,500,000; Full time

Benefits offered by this job

Job summary

PwC Acceleration Center India is seeking a hands-on GenAI engineer to build LLM-powered solutions inside real client environments. You’ll embed within an enterprise client's team to ship end-to-end AI apps, agents, and RAG pipelines, moving from prototype to production-ready solutions with AI tooling as a default.

You will collaborate with client teams and senior engineers, designing pipelines, integrating data, and iterating quickly based on user feedback and evaluation results.

Qualifications

• Hands-on GenAI/LLM engineering in production or near-production.
• Experience building with LLMs on AWS Bedrock and provider APIs.
• At least one agent framework experience.
• Experience creating RAG pipelines with embeddings and vector DBs.
• Prominent prompt engineering and basic model evaluation skills.
• Strong Python; some TypeScript for backend/light frontend APIs.
• Solid data fundamentals across structured and unstructured data.
• Comfort with ambiguity and a bias to build.

Responsibilities

• Build LLM-powered applications, agents, and automations for client problems.
• Design and implement RAG pipelines using embeddings and vector search.
• Use agent frameworks to orchestrate multi-step workflows and tools.
• Write clean Python and some TypeScript across backend and light frontend.
• Perform prompt engineering and evaluate AI performance.
• Integrate enterprise data into AI solutions.
• Iterate quickly on real user feedback and document work.
• Collaborate with client teams via Teams, Slack, and email.

Skills

GenAI/LLM engineering

LLM on AWS Bedrock

Agent framework

RAG pipelines

Prompt engineering

Python

TypeScript

Data fundamentals

Bias to build

Tools

Pinecone

pgvector

Weaviate

Qdrant

OpenSearch

Semantic search

React

TypeScript

SQL/NoSQL

Docker

Kubernetes

CI/CD

Terraform

Databricks

Snowflake

Job description

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).
• 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

• Retrieval & RAG: embeddings, vector databases (Pinecone, pgvector, Weaviate, Qdrant, OpenSearch), semantic search
• Frontend: React, TypeScript
• Data: SQL / NoSQL, data pipelines, structured and unstructured data
• Good to have: AWS SageMaker, Docker, Kubernetes, CI/CD, Terraform, Databricks, Snowflake
• 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

• 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.