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.