AI Architect With AWS - S

Key Responsibilities

• Design and define enterprise AI/ML and Generative AI architectures on AWS.
• Lead solution architecture for AI use cases including intelligent automation, predictive analytics, recommendation systems, NLP, computer vision, and GenAI applications.
• Architect and implement scalable AI platforms using AWS AI/ML services.
• Design Retrieval-Augmented Generation (RAG), Agentic AI, and Large Language Model (LLM)-based applications.
• Establish AI governance, security, compliance, and Responsible AI frameworks.
• Define MLOps best practices for model development, deployment, monitoring, and lifecycle management.
• Collaborate with data engineering teams to build AI-ready data platforms and pipelines.
• Provide technical leadership, architecture reviews, and guidance on AI adoption strategies.
• Optimize AI workloads for performance, scalability, reliability, and cost efficiency.
• Evaluate emerging AI technologies and define enterprise AI roadmaps.

Key Technical Responsibilities

AWS AI & Machine Learning

• Amazon Bedrock
• Amazon SageMaker
• Amazon Comprehend
• Amazon Lex
• Amazon Rekognition
• Amazon Kendra
• Amazon Textract
• Amazon Personalize
• Amazon Forecast

Generative AI & LLMs

• Large Language Models (LLMs)
• Foundation Models
• Prompt Engineering
• RAG Architectures
• AI Agents / Agentic Workflows
• Fine-Tuning and Model Customization
• Vector Databases
• Knowledge Graphs

Cloud & Architecture

• AWS Well-Architected Framework
• Multi-Account AWS Architecture
• Microservices and Event-Driven Architecture
• Serverless Architectures
• API Management
• Containerization using Docker and Kubernetes (EKS)

MLOps & DevOps

• CI/CD for AI workloads
• AWS CodePipeline
• AWS CodeBuild
• GitHub Actions
• Terraform / CloudFormation
• Model Monitoring and Drift Detection
• ML Lifecycle Management

Required Skills & Qualifications

Technical Skills

• Strong experience designing enterprise AI solutions on AWS.
• Deep understanding of Machine Learning, Deep Learning, NLP, and Generative AI.
• Hands-on experience with Amazon Bedrock and SageMaker.
• Experience architecting RAG-based and LLM-powered applications.
• Proficiency in Python and AI/ML libraries such as TensorFlow, PyTorch, Scikit-learn, LangChain, and LlamaIndex.
• Knowledge of vector databases such as Pinecone, OpenSearch Vector Engine, Weaviate, or Chroma.
• Experience with API integration, cloud-native application design, and distributed systems.
• Strong understanding of cloud security, IAM, encryption, networking, and governance.