At Deeploy we help organizations deploy AI they can trust. As an Implementation Engineer youll own the technical journey from first proof-of-concept to full platform adoption turning complex customer environments into working compliant AI governance setups. No two customers are the same and neither are the solutions youll build.
This is a hands-on role for someone equally comfortable in a customer conversation and a deep technical implementation. Youll engage stakeholders across data science engineering legal and compliance translating their needs into concrete implementations on the Deeploy platform. Youll report directly to the COO and work closely with sales and product giving you direct influence over both customer outcomes and how the product evolves. This role is hybrid based in Utrecht.
Why Deeploy
AI is being rolled out everywhere but most of it is a black box. Deeploy exists to change that. We build the technical foundation that makes AI truly responsible: helping companies detect understand and prevent the harms caused by misaligned AI systems before they impact the real world.
Were an AI-native team. That means we dont just build for AI we aim to be a frontrunner in our own work. Were constantly rethinking how we operate and we expect everyone on the team to be part of that evolution.
What you will do
Design and deliver proof-of-concepts that demonstrate measurable value within a customers existing AI infrastructure
Own onboarding and implementation end-to-end from scoping through go-live
Build trusted relationships with technical and non-technical stakeholders across customer accounts
Identify expansion opportunities and flag them to the sales team with clear context
Translate customer feedback and implementation patterns into product insights
Build reusable playbooks templates and integration guides that scale the teams capacity
Who we are looking for
This role sits at an unusual intersection: deep enough to debug an MLflow integration sharp enough to explain model risk to a CFO. If youve operated in that gap before and enjoyed it youll thrive here.
Must-Haves
2-5 years in a solution engineering implementation or technical customer success role
Hands-on experience with data AI or ML workflows in a production context
Comfort working across technical stakeholders (data scientists ML engineers IT) and business stakeholders simultaneously
Fluency in English and Dutch
A degree in a technical field (Engineering Data Science Econometrics or similar)
Nice-to-Haves
Experience in regulated industries (e.g. finance healthcare public sector)
Familiarity with AI compliance frameworks (e.g. EU AI Act model risk management)
Experience in a start-up or fast-paced scale-up environment
What to expect from us
Shape the Future of AI: Work at the forefront of AI governance transparency and compliance helping organizations innovate responsibly and create meaningful impact.
Collaborative Culture: Be part of a team that values openness inclusivity and collaboration. At Deeploy every voice matters and diverse perspectives drive our innovation.
Growth Opportunities: Access opportunities to learn develop and advance your career in a fast-growing company dedicated to transforming the AI landscape.
Competitive Benefits: Enjoy a competitive salary stock options and a comprehensive benefits package including a pension plan.
Flexibility and Balance: Thrive in a flexible hybrid work environment that supports your personal and professional well-being.
Our Hiring Process
We value efficiency and transparency. Heres what to expect:
Application: Share your LinkedIn/CV and any personal projects youre proud of.
Introductory Call: A 20-minute chat to introduce our team and learn more about you.
Personal & Team Fit: A 60-minute chat with 2 team members you would work with in the future
Case Study: Prepare a case (timeboxed to 3 hours) and present it in a follow-up meeting with two team members.
Offer: If were a fit well extend an offer and welcome you aboard!
Got questions Reach out anytime at .
Required Experience:
IC
Customer Engineer AI Governance (Hybrid)At Deeploy we help organizations deploy AI they can trust. As an Implementation Engineer youll own the technical journey from first proof-of-concept to full platform adoption turning complex customer environments into working compliant AI governance setups. No...
Customer Engineer AI Governance (Hybrid)
At Deeploy we help organizations deploy AI they can trust. As an Implementation Engineer youll own the technical journey from first proof-of-concept to full platform adoption turning complex customer environments into working compliant AI governance setups. No two customers are the same and neither are the solutions youll build.
This is a hands-on role for someone equally comfortable in a customer conversation and a deep technical implementation. Youll engage stakeholders across data science engineering legal and compliance translating their needs into concrete implementations on the Deeploy platform. Youll report directly to the COO and work closely with sales and product giving you direct influence over both customer outcomes and how the product evolves. This role is hybrid based in Utrecht.
Why Deeploy
AI is being rolled out everywhere but most of it is a black box. Deeploy exists to change that. We build the technical foundation that makes AI truly responsible: helping companies detect understand and prevent the harms caused by misaligned AI systems before they impact the real world.
Were an AI-native team. That means we dont just build for AI we aim to be a frontrunner in our own work. Were constantly rethinking how we operate and we expect everyone on the team to be part of that evolution.
What you will do
Design and deliver proof-of-concepts that demonstrate measurable value within a customers existing AI infrastructure
Own onboarding and implementation end-to-end from scoping through go-live
Build trusted relationships with technical and non-technical stakeholders across customer accounts
Identify expansion opportunities and flag them to the sales team with clear context
Translate customer feedback and implementation patterns into product insights
Build reusable playbooks templates and integration guides that scale the teams capacity
Who we are looking for
This role sits at an unusual intersection: deep enough to debug an MLflow integration sharp enough to explain model risk to a CFO. If youve operated in that gap before and enjoyed it youll thrive here.
Must-Haves
2-5 years in a solution engineering implementation or technical customer success role
Hands-on experience with data AI or ML workflows in a production context
Comfort working across technical stakeholders (data scientists ML engineers IT) and business stakeholders simultaneously
Fluency in English and Dutch
A degree in a technical field (Engineering Data Science Econometrics or similar)
Nice-to-Haves
Experience in regulated industries (e.g. finance healthcare public sector)
Familiarity with AI compliance frameworks (e.g. EU AI Act model risk management)
Experience in a start-up or fast-paced scale-up environment
What to expect from us
Shape the Future of AI: Work at the forefront of AI governance transparency and compliance helping organizations innovate responsibly and create meaningful impact.
Collaborative Culture: Be part of a team that values openness inclusivity and collaboration. At Deeploy every voice matters and diverse perspectives drive our innovation.
Growth Opportunities: Access opportunities to learn develop and advance your career in a fast-growing company dedicated to transforming the AI landscape.
Competitive Benefits: Enjoy a competitive salary stock options and a comprehensive benefits package including a pension plan.
Flexibility and Balance: Thrive in a flexible hybrid work environment that supports your personal and professional well-being.
Our Hiring Process
We value efficiency and transparency. Heres what to expect:
Application: Share your LinkedIn/CV and any personal projects youre proud of.
Introductory Call: A 20-minute chat to introduce our team and learn more about you.
Personal & Team Fit: A 60-minute chat with 2 team members you would work with in the future
Case Study: Prepare a case (timeboxed to 3 hours) and present it in a follow-up meeting with two team members.
Offer: If were a fit well extend an offer and welcome you aboard!