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.
Robust 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
Robust 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