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, AI/ML frameworks, data engineering, and DevOps/MLOps. The position offers opportunities for professional development and a flexible work environment.

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
• Skilled in distributed model training with multi-GPU clusters and optimization for high-performance inference
• Proficient in building data pipelines with Amazon S3, AWS Glue, Lake Formation, and Redshift for AI and ML workloads
• Experienced 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
• Knowledgeable in AI ethics, bias detection, and accountability using tools like SageMaker Clarify
• Up-to-date 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
• Skilled 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
• Address customer's strategic and tactical needs around cloud technologies
• Grow your technical and interpersonal skills by addressing customer challenges
• Strengthen your personal brand through thought leadership activities

Preferred Qualifications

• Passion for technology, with a demonstrated ability to quickly learn and stay up to date with industry trends
• Data certification is a major advantage (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