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HomeCompaniesGamingtecFounding AI Platform Engineer (MLOps / Backend)

Founding AI Platform Engineer (MLOps / Backend)

Gamingtec · Active · BambooHR

Job facts

FieldValue
CompanyGamingtec
TitleFounding AI Platform Engineer (MLOps / Backend)
Normalized title-
Department / teamIT
Location-
Work model-
Employment typeFull Time
Salary-
Statusactive
ATS providerBambooHR
Posted / first seen2026-03-23 / 2026-05-30
Changed / last seen2026-05-30 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Gamingtec.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through BambooHR.Open
Provider filtered searchThe same provider as a filtered job collection.Open
Department jobsActive postings in IT.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyGamingtec
Source896d31a8-8934-4e51-9522-8e7b6f403bdc
ATS providerBambooHR

Description

Are you ready to take your MLOps skills to the next level? We are hiring a Founding AI Platform Engineer to own the systems that make our ML and GenAI products reliable, deployable, observable, and scalable. This role sits at the intersection of backend engineering, infrastructure, MLOps, and product delivery. You will build the production layer around training, evaluation, deployment, serving, CI/CD, experimentation, and monitoring. In a team of our size, this role spans backend services, infrastructure, tooling, and reliability work. Your job is to make sure promising ML and GenAI capabilities become stable, customer-ready systems. All you need is: Strong software engineering background with experience building and operating production systems; Experience with backend services, cloud infrastructure, CI/CD, testing, observability, and automation; Strong Python skills and comfort working across services, tooling, infrastructure, and operational workflows; Good judgment about reliability, performance, maintainability, and cost tradeoffs; Ability to collaborate closely with ML and product teams and move ambiguous work to completion; High ownership, attention to detail, and a bias toward simplifying and strengthening systems. What would be an advantage: Experience with MLOps workflows for model training, evaluation, deployment, and monitoring; Experience serving ML models or LLM applications in production; Experience with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms; Experience with agent evaluation, prompt versioning, retrieval/search infrastructure, or vector-backed systems; Experience supporting customer-facing APIs or SaaS platform infrastructure. Your daily adventures will look like: Build and maintain the infrastructure and tooling used to train, evaluate, deploy, and monitor ML models and GenAI services; Own production services, APIs, and pipelines that power recommendations, agent workflows, and customer-facing integrations; Improve CI/CD, testing, release workflows, rollback processes, and environment management; Establish observability across service health, model behaviour, agent quality, latency, cost, and failure modes; Build reproducibility and lifecycle practices for models, prompts, datasets, configurations, and releases; Support experimentation and measurement infrastructure so product and ML changes can be evaluated cleanly; Improve reliability, scalability, security, performance, and cost efficiency across the stack; Troubleshoot production issues end-to-end and turn recurring pain points into durable engineering improvements; Help define the platform and engineering standards the company will rely on as it grows. What Success Looks Like in the First 6 Months: Shipping a model or GenAI change to production becomes faster, safer, and less manual; Core services and AI workflows are observable and easier to debug; The platform supports more usage with better reliability and lower operational friction; Engineers spend less time fighting infrastructure and deployment issues and more time shipping product; You become the person who can see platform, reliability, and scaling risks early and address them before they become problems. And this is how our interview process goes: A 30-minute interview with a member of our HR team to get to know you and your experience; A 1-hour technical interview; A final interview to gauge your fit with our culture and working style. Sounds interesting? Do not hesitate to apply or contact us if you have any questions!

Full job record

Job IDc7f576fe136cce7e1b344f99eafd4dcc05b887a8
Org ID9b4c695e-9475-4d2f-b747-4bfc2409fe1b
Source ID896d31a8-8934-4e51-9522-8e7b6f403bdc
Board ID896d31a8-8934-4e51-9522-8e7b6f403bdc
Providerbamboohr
Provider Job Key503
TitleFounding AI Platform Engineer (MLOps / Backend)
Normalized Title
Statusactive
Activeyes
Location Text
DepartmentIT
Team
Employment Typefull_time
Workplace Type
Remote Policy
Country
Region
City
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://gamingtec.bamboohr.com/careers/503
Apply URLhttps://gamingtec.bamboohr.com/careers/503
First Seen At2026-05-30 05:58:31Z
Last Seen At2026-06-06 10:32:39Z
Last Checked At2026-06-06 10:32:39Z
Last Changed At2026-05-30 05:58:31Z
Inactive At
Source Posted At2026-03-23 00:00:00Z
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=bamboohr/board=gamingtec/date=2026-06-06/2026-06-06T10-32-37-074Z-dfe38b2da0f0ae577772848d0a527b1f03e39dec5b9335fed89a1c9499a36334.json
Event Fields
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    "description": "<p><span style=\"font-family: arial, helvetica, sans-serif; font-size: 12pt; font-weight: bold\">Are you ready to take your MLOps skills to the next level?</span></p>\n<p><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">We are hiring a Founding AI Platform Engineer to own the systems that make our ML and GenAI products reliable, deployable, observable, and scalable. This role sits at the intersection of backend engineering, infrastructure, MLOps, and product delivery. You will build the production layer around training, evaluation, deployment, serving, CI/CD, experimentation, and monitoring. In a team of our size, this role spans backend services, infrastructure, tooling, and reliability work. Your job is to make sure promising ML and GenAI capabilities become stable, customer-ready systems.<br><br></span></p>\n<p><span style=\"font-family: arial, helvetica, sans-serif; font-size: 12pt; font-weight: bold\">All you need is:</span></p>\n<ul>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Strong software engineering background with experience building and operating production systems;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Experience with backend services, cloud infrastructure, CI/CD, testing, observability, and automation;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Strong Python skills and comfort working across services, tooling, infrastructure, and operational workflows;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Good judgment about reliability, performance, maintainability, and cost tradeoffs;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Ability to collaborate closely with ML and product teams and move ambiguous work to completion;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">High ownership, attention to detail, and a bias toward simplifying and strengthening systems.</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-family: arial, helvetica, sans-serif; font-size: 12pt; font-weight: bold\">What would be an advantage:</span></p>\n<ul>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Experience with MLOps workflows for model training, evaluation, deployment, and monitoring;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Experience serving ML models or LLM applications in production;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Experience with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Experience with agent evaluation, prompt versioning, retrieval/search infrastructure, or vector-backed systems;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Experience supporting customer-facing APIs or SaaS platform infrastructure.</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-family: arial, helvetica, sans-serif; font-size: 12pt; font-weight: bold\">Your daily adventures will look like:</span></p>\n<ul>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Build and maintain the infrastructure and tooling used to train, evaluate, deploy, and monitor ML models and GenAI services;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Own production services, APIs, and pipelines that power recommendations, agent workflows, and customer-facing integrations;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Improve CI/CD, testing, release workflows, rollback processes, and environment management;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Establish observability across service health, model behaviour, agent quality, latency, cost, and failure modes;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Build reproducibility and lifecycle practices for models, prompts, datasets, configurations, and releases;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Support experimentation and measurement infrastructure so product and ML changes can be evaluated cleanly;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Improve reliability, scalability, security, performance, and cost efficiency across the stack;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Troubleshoot production issues end-to-end and turn recurring pain points into durable engineering improvements;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Help define the platform and engineering standards the company will rely on as it grows.</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-family: arial, helvetica, sans-serif; font-size: 12pt; font-weight: bold\">What Success Looks Like in the First 6 Months:</span></p>\n<ul>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Shipping a model or GenAI change to production becomes faster, safer, and less manual;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Core services and AI workflows are observable and easier to debug;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">The platform supports more usage with better reliability and lower operational friction;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">Engineers spend less time fighting infrastructure and deployment issues and more time shipping product;</span></li>\n<li><span style=\"color: rgb(81, 82, 87); font-family: arial, helvetica, sans-serif; font-size: 12pt\">You become the person who can see platform, reliability, and scaling risks early and address them before they become problems.</span></li>\n</ul>\n<p><span style=\"font-family: arial, helvetica, sans-serif; font-size: 12pt; font-weight: bold\"><br>And this is how our interview process goes:</span></p>\n<ul>\n<li><span style=\"color: rgb(81, 82, 87); 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