Gen AI Engineer

PwC; Hyderabad, Bengaluru; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

PwC in Hyderabad, India is seeking a senior AI/ML engineer to design and deliver Generative AI solutions across enterprise scale. The role focuses on LLMs, RAG, semantic search, and knowledge graphs to integrate AI into business processes.

The ideal candidate has 5–8 years of AI experience, strong Python skills, and hands-on experience with LangGraph, FastAPI, document AI tools, and MLOps practices to ship production GenAI systems with governance and security.

Qualifications

• Minimum Degree Required: B.E/B.Tech
• Minimum Years of Experience: 5-8 years
• 4+ years of experience working with AI solutions

Responsibilities

• Design and implement Generative AI solutions leveraging LLMs, RAG, semantic search, knowledge graphs, and enterprise knowledge integration.
• Implement Agentic AI solutions with autonomous workflows, multi-agent orchestration, guardrails, and human-in-the-loop controls.
• Establish prompt engineering, management, evaluation, versioning, and lifecycle practices.
• Drive adoption of DevOps and MLOps with CI/CD, IaC, monitoring, and release governance.
• Implement responsible AI and AI governance including risk assessments, explainability, monitoring, and regulatory readiness.
• Support technical configuration activities including group/security configuration and data definition

Skills

Python

LLM applications

Workflow orchestration

Document AI

LLM evaluation

Prompt engineering

LLM security

Production delivery

Education

B.E./B.Tech

Tools

LangGraph

Pydantic v2

FastAPI

PyMuPDF

pypdf

Tesseract

PaddleOCR

Docker

Kubernetes

Helm

GitHub Actions

pytest

Job description

Additional Job Description

Minimum Degree Required: B.E/B.Tech

Degree Preferred: B.E/B.Tech

Minimum Years of Experience: 5-8 Years

• 4+ year of experience working with AI solutions

Required Technical Knowledge / Skills

• Design and implement Generative AI solutions leveraging large language models, Retrieval-Augmented Generation architecture patterns, semantic search, knowledge graphs, and enterprise knowledge integration.
• Implement Agentic AI solutions, including autonomous AI workflows, multi-agent orchestration, agentic frameworks, tool integration, guardrails, and human-in-the-loop controls for enterprise use cases.
• Establish prompt engineering, prompt management, evaluation, versioning, reuse, and lifecycle practices for scalable Generative AI delivery.
• Drive adoption of DevOps and MLOps practices, including CI/CD, infrastructure as code, automated testing, model deployment automation, monitoring, alerting, and release governance.
• Implement responsible AI and AI governance practices, including AI risk assessments, model explainability, transparency, validation, monitoring, and regulatory readiness for AI systems.
• Ability to support technical configuration activities including group and security configuration, database rules, validations, calculation logic, and reference data definition

Strongly preferred:

• Python: Strong hands-on Python experience, including async (asyncio/anyio), type hints, and Pydantic v2. Experience building FastAPI services with versioned API contracts.
• LLM application development: Hands-on experience building LLM applications with Pydantic AI or similar frameworks. Includes typed, schema validated outputs and handling malformed model responses through retries, repair, and fallbacks.
• Workflow orchestration: Experience building multi-step LLM pipelines with LangGraph.
• Document AI: Extracting data from PDFs, scans, images, and Word files using OCR (Tesseract, PaddleOCR) and PDF tooling (PyMuPDF, pypdf). Includes deciding when to use native text and when to use OCR.
• LLM evaluation: Building ground-truth datasets, field-level scoring, baseline comparisons, and CI regression gates, so prompt and model changes are measured, not guessed.
• Prompt engineering: Designing, versioning, and tuning prompts for accuracy, token cost, and latency.
• LLM security: Mitigating prompt injection, handling untrusted input safely, and returning safe error responses.
• Production delivery: A track record of shipping GenAI systems to production. Sound judgment on trade-offs between quality, latency, cost, and scalability, including batching, retries, confidence thresholds, and routing to human review.

Preferred Skills:

• Docker and Kubernetes (Helm, GitOps), plus CI/CD with GitHub Actions.
• Testing with pytest, including fixture-driven tests and mocked LLM clients.
• Integrating with enterprise workflow platforms such as Appian, or other downstream systems with strict API contracts.
• Observability for LLM calls, including tracing and token, and cost tracking.

Certifications (optional):

• A cloud AI/ML certification, such as Microsoft Azure AI Engineer Associate, AWS Certified Machine Learning Engineer Associate, or Google Cloud Professional Machine Learning Engineer
• Certified Kubernetes Application Developer (CKAD)
• Databricks Certified Generative AI Engineer Associate, or a similar vendor GenAI certification