AWS AI Engineer

PwC; Pune District, Gurugram District, Bengaluru; Hybrid; INR 700,000 - 1,200,000; Full time

Job summary

PwC is seeking an AWS Gen AI Engineer to design and develop generative AI apps using Bedrock, SageMaker and related AWS services. This hybrid role covers Pune and other Indian locations with UK shift alignment.

You will build RAG pipelines, leverage Bedrock Knowledge Bases, and implement prompt engineering and guardrails. Strong Python and ML framework skills are essential, with 3+ years on AWS AI/ML services.

Qualifications

• Bachelor's degree in CS/DS/AI or related field.
• 5+ years of software or ML engineering experience.
• 3+ years hands-on with AWS AI/ML services.
• Hands-on with Bedrock and foundation models.
• Experience with SageMaker training, fine-tuning, deployment (JumpStart, HyperPod).

Responsibilities

• Design and develop GenAI apps using Bedrock and SageMaker.
• Build RAG pipelines with Bedrock Knowledge Bases.
• Develop ML models with SageMaker (training, fine-tuning, deployment).
• Implement prompt engineering and optimization.
• Configure Bedrock Guardrails for safety and compliance.
• Integrate GenAI into production apps using LangChain, LlamaIndex, Strands Agents.
• Provide tech guidance to associates on AI/ML best practices.

Skills

AWS Bedrock

SageMaker

Python

Prompt Engineering

RAG pipelines

LangChain

Vector Stores

Knowledge Bases

LLM architectures

Education

Bachelors in CS/DS/AI

Tools

OpenSearch Serverless

Pinecone

JumpStart

HyperPod

LangChain

LlamaIndex

Strands Agents

Job description

Role : AWS AI Engineer

Work Mode : Hybrid

Locations : Pune, Gurugram, Bangalore, Hyderabad, Chennai, Kolkata

Shift : UK Shift

Role & responsibilities

We are seeking a skilled and experienced AWS Gen AI Engineer to design and develop generative AI applications leveraging Amazon Bedrock, Bedrock AgentCore, and SageMaker.

The ideal candidate will have hands‑on experience building GenAI applications using Bedrock foundation models, RAG pipelines with Bedrock Knowledge Bases, and prompt engineering workflows with Bedrock Guardrails.

Key Responsibilities

• Design and develop generative AI applications using Amazon Bedrock, Bedrock AgentCore, and foundation models.
• Build and optimize RAG pipelines using Bedrock Knowledge Bases with agentic retrieval, smart parsing, and multi-modal content support.
• Develop custom ML models using SageMaker (training, fine‑tuning, and deployment via JumpStart and HyperPod).
• Implement prompt engineering, optimization, and fine‑tuning workflows; leverage Intelligent Prompt Routing for cost optimization.
• Configure and manage Bedrock Guardrails for content safety, PII filtering, and hallucination mitigation with Automated Reasoning checks.
• Design data ingestion pipelines connecting enterprise sources (S3, SharePoint, Confluence, Google Drive, OneDrive, Web Crawler) to Knowledge Bases.
• Conduct model evaluation, benchmarking, and A/B testing across foundation models.
• Integrate GenAI capabilities into production applications using orchestration frameworks (LangChain, LlamaIndex, Strands Agents).
• Provide technical guidance to associates on AI/ML best practices.

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, AI/ML, or related field.
• 5+ years of experience in software engineering or ML engineering.
• 3+ years of hands‑on experience with AWS AI/ML services.
• Strong proficiency in building GenAI applications using Amazon Bedrock and foundation models.
• Hands‑on experience with SageMaker for model training, fine‑tuning, and deployment (JumpStart, HyperPod).
• Experience building RAG pipelines with Bedrock Knowledge Bases and vector stores (OpenSearch Serverless, Pinecone, or similar).
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
• Experience with orchestration frameworks (LangChain, LlamaIndex, CrewAI, or Strands Agents).
• Experience with prompt engineering, optimization, and evaluation techniques.
• Understanding of LLM architectures, tokenization, and inference optimization.
• Experience configuring Bedrock Guardrails for content safety and compliance.
• Knowledge of data engineering for AI/ML (ETL, data preprocessing, feature engineering).
• Strong communication skills and ability to articulate technical decisions.

Nice to Have

• AWS Machine Learning Specialty or AI Practitioner certification.
• Experience with multi-modal AI (vision, audio, text) on Bedrock.
• Familiarity with model monitoring, SageMaker MLOps pipelines, and AI observability.
• Experience with AI tools in development lifecycles (GitHub CoPilot, Cursor, Amazon Q Developer).
• Knowledge of responsible AI frameworks, Automated Reasoning, and bias mitigation.