AI Architect with AWS - S

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.

• Strong 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.