Description

Role Summary

The GenAI Engineer / Architect will design, develop, deploy, and operationalize enterprise-grade Generative AI solutions leveraging Amazon Bedrock, Amazon SageMaker, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and Responsible AI practices.

The ideal candidate will have hands-on experience building AI applications and agentic workflows in AWS, including model evaluation, monitoring, governance, and production deployment.

Responsibilities

• Design and implement end-to-end Generative AI solutions using Amazon Bedrock and Amazon SageMaker.
• Build and deploy AI Agents and multi-agent workflows using Amazon Bedrock Agents.
• Develop RAG architectures using Bedrock Knowledge Bases, embeddings, vector databases, and enterprise data sources.
• Fine-tune, evaluate, and deploy models using Amazon SageMaker.
• Design and implement document ingestion pipelines, metadata extraction, and enterprise search capabilities.
• Develop validation frameworks to assess response quality, groundedness, hallucinations, citations, and agent performance.
• Configure Amazon Bedrock Guardrails and implement Responsible AI and governance controls.
• Implement monitoring and observability using Amazon CloudWatch, SageMaker Model Monitor, and related AWS services.
• Monitor model performance, latency, cost, drift, and operational metrics.
• Deploy GenAI workloads using SageMaker Endpoints, Lambda, ECS/EKS, API Gateway, and Step Functions.
• Build CI/CD and MLOps pipelines using SageMaker Pipelines, GitHub Actions, AWS CodePipeline, and Terraform.
• Integrate AI solutions with enterprise platforms such as SharePoint, APIs, and business applications.
• Lead POCs, architecture reviews, technical assessments, and knowledge transfer activities.

Required Skills

Generative AI

• RAG architectures, embeddings, retrieval optimization, and vector search.
• AI Agents, agent orchestration, tool calling, and workflow automation.
• Prompt engineering, model evaluation, hallucination detection, and response validation.

AWS GenAI Stack

• Amazon Bedrock (Agents, Knowledge Bases, Guardrails, Foundation Models).
• Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Model Monitor).
• AWS Lambda, API Gateway, Step Functions, ECS/EKS.

Data & Search

• OpenSearch, PostgreSQL/pgvector, Redis, Pinecone, or similar vector databases.
• OCR, document processing, metadata enrichment, and enterprise search.

Monitoring & Governance

• AI observability, model monitoring, drift detection, and performance analysis.
• Responsible AI, NIST AI RMF, security, and governance frameworks.

DevOps & Cloud

• AWS (required), Azure (preferred).
• CI/CD, MLOps, Terraform, GitHub Actions, Kubernetes, and Docker.

Required Certifications

• AWS Certified Machine Learning – Specialty
• AWS Certified Solutions Architect – Associate or Professional
• AWS Certified AI Practitioner (or equivalent)
• Kubernetes CKA/CKAD

Preferred Certifications

• AWS Certified DevOps Engineer Professional
• Azure AI Engineer (AI-102)
• Azure Data Scientist (DP-100)
• Azure Solutions Architect (AZ-305)

Desired Experience

• Building production-grade AI Agents using Amazon Bedrock.
• Implementing Bedrock Knowledge Bases and enterprise RAG solutions.
• Fine-tuning and deploying models with Amazon SageMaker.
• Establishing evaluation, monitoring, and governance frameworks for GenAI applications.
• Deploying scalable AI solutions in AWS production environments

Key Skills

Education

Any Graduate