AWS Gen AI Engineer

PwC; India; Hybrid; INR 3,000,000 - 5,500,000; Full time

Job summary

PwC in India is seeking an experienced AWS Gen AI Engineer to lead generative AI application development, driving innovation with Bedrock, AgentCore, SageMaker, and production-grade RAG pipelines. You'll design multi-agent orchestration, custom model training, and responsible AI practices, collaborating with product, data science, and platform teams to deliver scalable AI solutions.

The role demands deep expertise in GenAI architectures, MLOps, and cost-optimized prompt frameworks, with

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.
• 10+ years of experience in software engineering or ML engineering.
• 5+ years of hands-on experience with AWS AI/ML services.

Responsibilities

• Lead the design and architecture of generative AI applications using Amazon Bedrock, Bedrock AgentCore, and SageMaker.
• Architect production-grade RAG pipelines using Bedrock Knowledge Bases with agentic retrieval, hybrid search, reranking, and multi-modal content (text, images, audio, video).
• Drive custom ML model development and fine-tuning strategies using SageMaker HyperPod, JumpStart, and Unified Studio.
• Define and implement prompt engineering frameworks; optimize cost with Intelligent Prompt Routing and Model Distillation (up to 500% faster, 75% cost reduction).
• Architect multi-agent orchestration patterns using Bedrock AgentCore with supervisor agents, RAG integration, code interpretation, and memory retention.
• Establish Bedrock Guardrails policies for responsible AI including content filtering, PII protection, Automated Reasoning checks, and hallucination mitigation.
• Lead the design of scalable data pipelines for AI/ML workloads using SageMaker Data Processing and Bedrock Data Automation.
• Establish AI/ML best practices, governance frameworks, and responsible AI guidelines.
• Lead and mentor GenAI engineering teams, driving technical excellence.
• Collaborate with product, data science, and platform teams to deliver AI-driven solutions.
• Evaluate emerging GenAI technologies (new foundation models, MCP integrations) and recommend adoption strategies.

Skills

AWS

GenAI architecture

Python

PyTorch

TensorFlow

Hugging Face

LangChain

Multi-agent orchestration

Education

Bachelor's or Master's degree in Computer Science/Data Science/AI

Tools

Bedrock (AWS)

Bedrock AgentCore

SageMaker

MLOps

HyperPod

JumpStart

Unified Studio

Job description

AWS Gen AI Engineer

About the Role

We are seeking an experienced AWS Gen AI Engineer to lead generative AI application development, driving innovation with Amazon Bedrock, Bedrock AgentCore, SageMaker, and production-grade RAG pipeline architectures.

The ideal candidate will have deep expertise in GenAI application architecture including multi-agent orchestration with AgentCore, custom model training on SageMaker HyperPod, and production RAG systems with agentic retrieval.

Key Responsibilities

• Lead the design and architecture of generative AI applications using Amazon Bedrock, Bedrock AgentCore, and SageMaker.
• Architect production-grade RAG pipelines using Bedrock Knowledge Bases with agentic retrieval, hybrid search, reranking, and multi-modal content (text, images, audio, video).
• Drive custom ML model development and fine-tuning strategies using SageMaker HyperPod, JumpStart, and Unified Studio.
• Define and implement prompt engineering frameworks; optimize cost with Intelligent Prompt Routing and Model Distillation (up to 500% faster, 75% cost reduction).
• Architect multi-agent orchestration patterns using Bedrock AgentCore with supervisor agents, RAG integration, code interpretation, and memory retention.
• Establish Bedrock Guardrails policies for responsible AI including content filtering, PII protection, Automated Reasoning checks, and hallucination mitigation.
• Lead the design of scalable data pipelines for AI/ML workloads using SageMaker Data Processing and Bedrock Data Automation.
• Establish AI/ML best practices, governance frameworks, and responsible AI guidelines.
• Lead and mentor GenAI engineering teams, driving technical excellence.
• Collaborate with product, data science, and platform teams to deliver AI-driven solutions.
• Evaluate emerging GenAI technologies (new foundation models, MCP integrations) and recommend adoption strategies.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.
• 10+ years of experience in software engineering or ML engineering.
• 5+ years of hands-on experience with AWS AI/ML services.
• Deep expertise in GenAI application architecture using Bedrock, Bedrock AgentCore, and foundation models.
• Extensive experience with SageMaker for custom model development, fine-tuning, MLOps, and HyperPod distributed training.
• Proven experience architecting production RAG pipelines with Bedrock Knowledge Bases (agentic retrieval, multi-modal support) and enterprise-grade reliability.
• Expert-level Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
• Strong experience with orchestration frameworks (LangChain, LlamaIndex, CrewAI, Strands Agents) and multi-agent patterns.
• Strong experience in prompt engineering, Model Distillation, Intelligent Prompt Routing, and evaluation at scale.
• Deep understanding of LLM architectures, fine-tuning approaches, RLHF, and inference optimization.
• Experience with Bedrock Guardrails, Automated Reasoning checks, and responsible AI practices.
• Excellent communication, leadership, and stakeholder management skills.
• Knowledge of Agile Methodology and Practices.

Nice to Have

• AWS Machine Learning Specialty certification.
• Experience with multi-modal AI, agentic AI architectures, and MCP integrations.
• Experience building MLOps pipelines using SageMaker Pipelines for production model management.
• Experience with AI tools in development lifecycles (GitHub CoPilot, Cursor, Amazon Q Developer).
• Published research or patents in AI/ML domain.
• Experience with enterprise AI governance, compliance frameworks, and FedRAMP/HIPAA-eligible AI workloads.