Senior Cloud Architect, ML/AI

Role Overview

• Lead the design and implementation of production-grade ML and Generative AI solutions on AWS (with awareness of multi-cloud environments).
• Act as a hands-on expert and trusted advisor for customers running AI/ML workloads at scale, from initial discovery through deployment and optimization.
• Translate complex business problems into cloud architectures that are secure, reliable, cost-efficient, and observable.
• Help evolve how DoiT uses AI/ML internally and with customers by turning one-off solutions into reusable patterns and “gravel roads” that influence the product roadmap.
• For Field Engineering, focus more on pre-sales, POVs, CloudBuild engagements, and partner-led growth motions.
• For Delivery, focus more on install base health, product adoption, proactive engagements, and account-team work.
• Own the technical success of engagements and ensure designs are production-ready (security, reliability, performance, cost).
• Partner with Account Executives, Solution Engineers, and Growth FDEs to shape and win opportunities across all four GTM pillars in-region (product adoption, new logo acquisition, install base expansion, partner-led growth).
• Serve as technical lead for extended POVs and CloudBuild engagements focused on AI/ML and GenAI, demonstrating clear value and de-risking customer adoption.
• Document and measure the business and technical impact of your work, tying AI/ML initiatives to clear customer outcomes (cost, performance, reliability, productivity).

Requirements

• 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions, including production workloads.
• Proven track record designing and operating large, distributed systems on AWS, selecting appropriate services and patterns to meet business and technical goals.
• Advanced proficiency with AWS services relevant to AI/ML and GenAI.
• Hands-on experience with Amazon Bedrock for deploying and scaling foundation models and Generative AI workloads.
• Experience fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker (including JumpStart).
• Strong prompt engineering skills and familiarity with rigorous model evaluation (quality, safety, performance).
• Understanding of agentic capabilities and patterns for AI agents that autonomously perform tasks and integrate with existing systems.
• Experience with Amazon Q Business and Amazon Q Developer (or similar tools) to accelerate insight generation and development workflows.
• In-depth knowledge of Amazon SageMaker components such as Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability.
• Proficiency integrating TensorFlow, PyTorch, and other ML frameworks with SageMaker for model development, fine-tuning, and deployment.
• Experience with distributed training (multi-GPU or multi-node) and performance optimization for inference.
• Strong data-engineering skills on AWS: Amazon S3, AWS Glue, Lake Formation, Redshift for AI/ML data pipelines.
• Experience building end-to-end AI/ML workflows using services like AWS Lambda, Step Functions, API Gateway, and containerized deployments on Amazon EKS / AWS Fargate.
• Hands-on experience with CI/CD for AI/ML using AWS CodePipeline, CodeBuild, SageMaker Pipelines, or similar.
• Proficiency in monitoring and operating AI systems using Amazon CloudWatch and SageMaker Model Monitor.
• Strong understanding of AI governance, security, and compliance on AWS, including IAM, KMS, and data privacy patterns.
• Familiarity with AI ethics and bias detection/mitigation (e.g., using SageMaker Clarify or similar tools).
• Working knowledge of Google Cloud AI tools (e.g., Vertex AI, Cloud AutoML, BigQuery ML) sufficient to reason about multi-cloud architectures and integration points.
• Proven ability to mentor peers, run enablement sessions, and collaborate across Sales, CS, and Product.
• Excellent communication skills across technical and business audiences; able to simplify complex ideas and influence decisions.
• Natural ownership mentality: you escalate early, resolve fast, and own the outcome.
• Demonstrated ability to work effectively in a remote-first, global environment.

Tech Stack

• Amazon Redshift
• AWS
• BigQuery
• Cloud
• Distributed Systems
• Node.js
• PyTorch
• Tensorflow

Benefits

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