Senior Cloud Architect, ML/AI

DoiT

15h•Remote

About The Position

As a Senior Cloud Architect, ML/AI, you will be part of our global Forward Deployed Engineering organization, working with rapidly growing companies in EMEA and around the world. Depending on business needs, this role may be aligned to either Field Engineering (pre-sales + GTM) or FDE Delivery (install base, product adoption, customer health), with a common technical bar and shared expectations. You will: 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, you will focus more on pre-sales, POVs, CloudBuild engagements, and partner-led growth motions. For Delivery, you will focus more on install base health, product adoption, proactive engagements, and account-team work.

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.

Nice To Haves

BA/BS degree in Computer Science, Mathematics, or a related technical field, or equivalent practical experience.
Additional data or AI certifications (e.g., AWS/GCP data certifications, reputable AI/ML programs such as Stanford, Coursera, Udacity, MIT, eCornell).
Experience with modern RLHF, advanced fine-tuning techniques, and hybrid AI architectures.
Familiarity with Hugging Face or similar open-source ecosystems integrated with AWS.
Prior experience as a ML Engineer, Data Scientist, or AI-focused Architect in a consulting or SaaS environment.
Experience with JIRA or similar tools for tracking work across delivery and product-feedback cycles.
Exposure to Agile practices and frameworks commonly used for SaaS and cloud delivery.

Responsibilities

Customer Outcomes & Technical Leadership
Lead discovery, architecture, and implementation for advanced ML and Generative AI workloads on AWS, including data, training, inference, and integration layers.
Own the technical success of your engagements: clearly define outcomes, make tradeoffs visible, and ensure designs are production-ready (security, reliability, performance, cost).
Provide opinionated guidance on GenAI architectures (e.g., Amazon Bedrock, SageMaker, Q) and how they integrate with customers’ existing systems and processes.
For Field Engineering: 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.
Build compelling technical narratives and demos that support revenue-generating motions, including co-sell initiatives with CSP partners.
For Delivery: Act as a named technical advisor for a portfolio of existing customers, working within account teams (Account Manager, CSM, FDE) to improve install base health and outcomes for AI/ML and GenAI workloads.
Lead proactive “Get FDE”–style engagements where AI/ML expertise is needed to unblock customers, reduce risk, or improve the impact of DoiT Cloud Intelligence.
Participate in structured account-team routines (e.g., objective setting, quarterly environment reviews) to keep AI/ML architectures aligned with customer goals and product adoption opportunities.
Product Adoption & Install-Base Impact (AI/ML & GenAI)
Recommend and implement AI/ML-related capabilities in DoiT Cloud Intelligence (e.g., CloudFlow, Insights, DataHub) as part of your customer engagements.
Document and measure the business and technical impact of your work, tying AI/ML initiatives to clear customer outcomes (cost, performance, reliability, productivity).
For Field Engineering: Design and run service-led product adoption plays that use AI/ML and GenAI projects to drive deeper adoption of DoiT’s platforms, in partnership with Growth leadership.
Ensure AI/ML-focused CloudBuild and POV engagements include mandatory product adoption playbooks, with clear activation and follow-up criteria.
For Delivery: Execute against delivery programs and automations that detect product struggles (e.g., customers failing to complete AI/ML workflows, incomplete CloudFlow pipelines) and turn those into targeted AI/ML advisory engagements.
Use every relevant delivery touchpoint to recommend and operationalize product adoption (e.g., Insights, automation, FinOps/CloudOps workflows) in collaboration with account teams.
Delivery Excellence, Practice Building & “Gravel Roads”
Maintain a high personal bar for delivery quality: clear scopes, realistic plans, strong communication, and crisp technical documentation.
Capture repeatable AI/ML patterns, reference architectures, and runbooks that other engineers can apply across customers.
For Field Engineering: Identify and validate “gravel road” solutions—custom AI/ML or GenAI integrations and patterns that should be elevated into standard offerings or product features.
Work with Product, R&D, and growth leaders to submit and champion these patterns into the roadmap, connecting field innovation to scalable packages and revenue engines.
For Delivery: Contribute to the FDE Delivery vision by turning recurring AI/ML implementation work into structured “gravel roads” (e.g., reusable CloudFlow patterns, Insights definitions, data pipelines) that can be productized.
Collaborate with FDE advocates and product teams to ensure field-built AI/ML solutions are vetted, documented, and, when appropriate, handed off for productization.
Collaboration, Partners & Cross-Functional Alignment
Collaborate closely with Sales, Customer Success, Product, and Business Systems Engineering to ensure AI/ML work is visible, repeatable, and connected to company priorities.
Communicate clearly with both technical and non-technical stakeholders, setting expectations and making risks and tradeoffs explicit.
For Field Engineering: Work with cloud partner teams (especially AWS) to align AI/ML initiatives to program funding, strategic bets, and co-sell motions—without compromising customer outcomes.
Provide technical leadership for partner-led opportunities involving GenAI and ML on AWS, ensuring DoiT’s value and DoiT Cloud Intelligence are central to the solution.
For Delivery: Coordinate with Account Managers, CSMs, TAMs, and other FDEs to ensure AI/ML engagements are sequenced correctly within broader account plans and install-base priorities.
Feed structured, field-derived feedback on product adoption barriers (especially for AI/ML capabilities) into Delivery leadership and product teams.
Operational Excellence & Ways of Working
Use and maintain the systems, templates, and workflows that support planning, observability, and quality across Customer Experience (e.g., JIRA, documentation standards, dashboards).
Contribute to internal enablement: teach other Doers about new AI/ML capabilities, share patterns, and help raise the bar globally for ML/AI expertise.
For Field Engineering: Ensure your AI/ML work is accurately reflected in pipelines, opportunities, and CloudBuild portfolios, enabling reliable reporting on technical win rates and influence on ARR.
Help improve forecast quality and POV coverage for AI/ML-related opportunities by maintaining good hygiene in the relevant systems.
For Delivery: Ensure AI/ML delivery engagements are logged, measured, and observable in the tools used for install-base health and product adoption tracking.
Participate in Delivery operating rhythms (e.g., team reviews, program updates) with clear, data-backed updates on your AI/ML work and impact.