Senior Cloud Architect, Delivery – GenAI

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Lead the creation and execution of production-quality ML and Generative AI solutions on AWS while considering multi-cloud environments.

• Serve as a hands-on expert and trusted consultant for clients managing AI/ML workloads at scale, from initial exploration to deployment and enhancement.

• Convert complex business challenges into secure, reliable, cost-effective, and observable cloud architectures.

• Contribute to the evolution of DoiT’s internal and customer-facing AI/ML initiatives by transforming isolated solutions into reusable frameworks and “gravel roads” that shape the product roadmap.

• Emphasize on install base health, product adoption, proactive engagements, and collaboration with account teams.

⛳️ Requirements

• Over 4 years of experience in designing, deploying, and managing cloud-based AI/ML solutions, including production-level workloads.

• Demonstrated success in designing and operating large, distributed systems on AWS, with the ability to choose suitable services and patterns that align with business and technical objectives.

• Advanced knowledge of AWS services pertinent to AI/ML and Generative AI.

• Practical experience with Amazon Bedrock for deploying and scaling foundational models and Generative AI workloads.

• Familiarity with fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker (including JumpStart).

• Strong skills in prompt engineering and a comprehensive understanding of thorough model evaluation (quality, safety, performance).

• Knowledge of agentic capabilities and methodologies for AI agents that autonomously carry out tasks and integrate with existing systems.

• Experience with Amazon Q Business and Amazon Q Developer (or similar tools) to expedite insight generation and development workflows.

• Extensive understanding of Amazon SageMaker components such as Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability.

• Proficient in 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 enhancement for inference.

• Strong data engineering skills on AWS, including Amazon S3, AWS Glue, Lake Formation, and Redshift for AI/ML data pipelines.

• Experience in building comprehensive 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 processes for AI/ML using AWS CodePipeline, CodeBuild, SageMaker Pipelines, or similar tools.

• Competency in monitoring and managing AI systems utilizing Amazon CloudWatch and SageMaker Model Monitor.

• Solid understanding of AI governance, security, and compliance on AWS, including IAM, KMS, and data privacy strategies.

• Familiarity with AI ethics and bias detection/mitigation (e.g., employing SageMaker Clarify or similar tools).

• Working knowledge of Google Cloud AI tools (e.g., Vertex AI, Cloud AutoML, BigQuery ML) sufficient for reasoning about multi-cloud architectures and integration points.

• Proven capability to mentor colleagues, conduct enablement sessions, and collaborate across Sales, Customer Success, and Product teams.

🏝️ Benefits

• Unlimited Vacation

• Flexible Working Options

• Health Insurance

• Parental Leave

• Employee Stock Option Plan

• Home Office Allowance

• Professional Development Stipend

• Peer Recognition Program