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.

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

📌 AI Architect with AWS - S (Chennai)
🏢 Tata Consultancy Services
📍 Chennai