Senior Cloud Architect, ML/AI

Summary

Join DoiT's Cloud Reliability Engineering team as a Senior Cloud Architect, contributing to the success of rapidly growing companies globally. Apply your expertise in architecting, deploying, and managing cloud-based AI/ML solutions on AWS, leveraging Generative AI and machine learning services. Grow your skills through dedicated learning time and internal initiatives, and strengthen your personal brand through thought leadership. This role requires extensive experience with AWS services, including SageMaker, Bedrock, and various data engineering tools, along with expertise in AI governance and security. The ideal candidate will also possess knowledge of Google Cloud AI tools and a passion for staying current with industry trends. This position offers a remote work environment with flexible hours and various benefits.

Requirements

• Minimum of 4+ years of experience in architecting, deploying, and managing cloud-based AI/ML solutions
• Expertise in architecting, developing, and troubleshooting large production-grade distributed systems on AWS
• Advanced proficiency in AWS, with a focus on Generative AI and machine learning services
• Certified AWS Solutions Architect Professional and/or AWS Machine Learning Specialty
• Proven track record of architecting, deploying, and optimizing complex cloud solutions for AI-driven workloads
• Expertise in Amazon Bedrock for deploying foundation models and managing scalable GenAI workloads
• Proficiency in fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker JumpStart and Hugging Face on AWS
• Experience leveraging Amazon Q (formerly AWS CodeWhisperer) Business and Developer for AI-powered coding productivity and automation
• In-depth knowledge of Amazon SageMaker, including Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability
• Skill in distributed model training with multi-GPU clusters and optimization for high-performance inference
• Proficiency in building data pipelines with Amazon S3, AWS Glue, Lake Formation, and Redshift for AI and ML workloads
• Experience in optimizing data preparation for large-scale AI model training and inference workflows
• Expertise in building end-to-end AI pipelines using AWS Lambda, Step Functions, and API Gateway for real-time AI inference and automation
• Familiarity with containerized AI deployments using Amazon EKS and AWS Fargate
• Hands-on experience with CI/CD pipelines for AI/ML workflows using AWS CodePipeline, CodeBuild, and SageMaker Pipelines
• Proficiency in monitoring and maintaining AI systems with Amazon CloudWatch and SageMaker Model Monitor
• Strong understanding of AI governance, incorporating IAM, AWS Key Management Service (KMS), and compliance frameworks
• Knowledge in AI ethics, bias detection, and accountability using tools like SageMaker Clarify
• Up-to-date knowledge on Generative AI advancements such as RLHF (Reinforcement Learning with Human Feedback), foundation model fine-tuning, and hybrid AI architectures
• Familiarity with integrating open-source tools like Hugging Face Transformers and AWS-native solutions
• Good knowledge and understanding of Google Cloud AI tools such as Vertex AI, Cloud AutoML, and BigQuery ML, enabling effective integration in multi-cloud environments
• Proven ability to mentor team members and enable cross-functional collaboration to foster technical growth and innovation
• Skill in creating knowledge-sharing platforms and hands-on workshops to enhance team capabilities

Responsibilities

• Architect, deploy, and manage cloud-based AI/ML solutions on AWS
• Develop and troubleshoot large production-grade distributed systems on AWS
• Select appropriate tools to address business problems at the correct scale
• Architect, deploy, and optimize complex cloud solutions for AI-driven workloads
• Deploy foundation models and manage scalable GenAI workloads using Amazon Bedrock
• Fine-tune and deploy LLMs and multimodal AI using Amazon SageMaker JumpStart and Hugging Face on AWS
• Leverage Amazon Q for AI-powered coding productivity and automation
• Build data pipelines with Amazon S3, AWS Glue, Lake Formation, and Redshift for AI and ML workloads
• Optimize data preparation for large-scale AI model training and inference workflows
• Build end-to-end AI pipelines using AWS Lambda, Step Functions, and API Gateway for real-time AI inference and automation
• Utilize containerized AI deployments using Amazon EKS and AWS Fargate
• Implement CI/CD pipelines for AI/ML workflows using AWS CodePipeline, CodeBuild, and SageMaker Pipelines
• Monitor and maintain AI systems with Amazon CloudWatch and SageMaker Model Monitor
• Apply strong understanding of AI governance, incorporating IAM, AWS Key Management Service (KMS), and compliance frameworks
• Demonstrate knowledge in AI ethics, bias detection, and accountability using tools like SageMaker Clarify
• Stay up-to-date on Generative AI advancements such as RLHF, foundation model fine-tuning, and hybrid AI architectures
• Integrate open-source tools like Hugging Face Transformers and AWS-native solutions
• Demonstrate good knowledge and understanding of Google Cloud AI tools such as Vertex AI, Cloud AutoML, and BigQuery ML
• Mentor team members and enable cross-functional collaboration to foster technical growth and innovation
• Create knowledge-sharing platforms and hands-on workshops to enhance team capabilities

Preferred Qualifications

• Passion for technology, with a demonstrated ability to quickly learn and stay up to date with industry trends
• Data certification (e.g., Stanford, Coursera, Udacity, MIT, eCornell, or any Data certification with AWS/GCP)
• BA/BS degree in Computer Science, Mathematics, Economics, or a related technical field, or equivalent practical experience

Benefits

• Unlimited Vacation
• Flexible Working Options
• Health Insurance
• Parental Leave
• Employee Stock Option Plan
• Home Office Allowance
• Professional Development Stipend
• Peer Recognition Program