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À propos
Senior Cloud Architect, ML/AI
will be an integral part of our global Forward Deployment Engineering team. This role is based remotely in the US, Colombia, Mexico, Canada, the UK, Ireland, Estonia, Sweden, the Netherlands, and Israel. The job is also open to contractors in Eastern Europe or Portugal.
About DoiT DoiT is a global technology company that works with cloud-driven organizations to leverage public cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state—from planning to production.
Delivering
DoiT Cloud Intelligence , the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency. With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide.
The Opportunity 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 .
Responsibilities 1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
Success Metrics & Objectives
Success metrics and specific objectives
for this role will be defined and updated on an
annual and quarterly
basis through the company planning cycle, pod charters, Weekly Operating Review (WOR) scorecards, and relevant regional/functional scorecards.
As a Senior Cloud Architect, you are expected to:
Understand the metrics and objectives that apply to your
function (Field Engineering or FDE Delivery)
and region.
Transparently report progress against those metrics.
Proactively propose and execute
corrective actions
when off track.
Qualifications
Experience
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.
AWS & GenAI / ML Expertise
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.
ML Pipelines, Data & MLOps
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 .
DevOps, MLOps, Governance & Security
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).
Multi-Cloud Awareness & Collaboration
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.
Soft Skills
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.
Bonus Points
Education & Certifications
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).
Expanded AI/ML & Dev Experience
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.
Tooling & Process
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.
Are you a Do’er? Be your truest self. Work on your terms. Make a difference.
We are home to a global team of incredible talent who work remotely and have the flexibility to have a schedule that balances your work and home life. We embrace and support leveling up your skills professionally and personally.
What does being a Do’er mean? We’re all about being entrepreneurial, pursuing knowledge, and having fun! Click here to learn more about our
core values .
Sounds too good to be true? Check out our
Glassdoor Page .
We thought so too, but we’re here and happy we hit that ‘apply’ button.
Full-time employee benefits include:
Unlimited Vacation
Flexible Working Options
Health Insurance
Parental Leave
Employee Stock Option Plan
Home Office Allowance
Professional Development Stipend
Peer Recognition Program
Many Do’ers, One Team DoiT unites as
Many Do’ers, One Team , where diversity is more than a goal—it's our strength. We actively cultivate an inclusive, equitable workplace, recognizing that each unique perspective enhances our innovation. By celebrating differences, we create an environment where every individual feels valued, contributing to our collective success.
#LI-Remote
Compétences linguistiques
- English
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