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
Solid 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.
Solid understanding of cloud security, IAM, encryption, networking, and governance.

📌 Ai Architect With Aws S Kochi
🏢 Tata Consultancy Services
📍 Kochi