Key Responsibilities

- Design and architect enterprise-scale AI/ML and Generative AI solutions on AWS.
- Develop cloud-native AI architectures that are scalable, secure, resilient, and cost-efficient.
- Lead end-to-end AI solution implementation including data ingestion, model training, deployment, monitoring, and optimization.
- Design and implement MLOps frameworks and AI deployment pipelines.
- Collaborate with business stakeholders to identify AI use cases and translate business requirements into technical solutions.
- Architect solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), chatbots, recommendation engines, and predictive analytics.
- Define cloud security, governance, compliance, and responsible AI best practices.
- Provide technical leadership and mentoring to development, data science, and engineering teams.
- Integrate AI services with enterprise applications, APIs, and third-party platforms.
- Conduct architectural reviews and establish technical standards and best practices.
- Optimize AI workloads for performance, scalability, reliability, and cost management.
- Stay updated with emerging AI/ML technologies, AWS innovations, and industry trends.

Required Skills & Qualifications

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence,



Data Science, Engineering, or a related field.
- 8+ years of experience in software engineering, cloud architecture, or data engineering.
- 3+ years of experience architecting AI/ML solutions on AWS.
- Solid understanding of AI, Machine Learning, Deep Learning, and Generative AI concepts.
- Hands-on experience with AWS services such as:

- Amazon Bedrock

- Amazon SageMaker

- AWS Lambda

- Amazon S3

- Amazon EC2

- Amazon ECS/EKS

- API Gateway

- Amazon Redshift

- Amazon Aurora

- Amazon DynamoDB

- AWS Glue

- Amazon Kinesis

- AWS IAM

- Amazon CloudWatch
- Experience designing and deploying LLM-based solutions.
- Strong knowledge of NLP, Recommendation Systems, Predictive Analytics, and Computer Vision.
- Experience with Python and AI/ML frameworks:

- TensorFlow

- PyTorch
- Scikit-learn

- LangChain

- Hugging Face
- Experience with MLOps and model lifecycle management.
- Knowledge of containerization technologies:

- Docker

- Kubernetes
- Experience with Infrastructure as Code:

- Terraform

- AWS CloudFormation
- Strong understanding of REST APIs and microservices architecture.
- Experience with CI/CD implementation for AI workloads.

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