Founding AI Platform Engineer – MLOps, Backend

Job not on LinkedIn

🕒 July 29

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

👻 Ghost score 44%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Gamingtec

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51 - 200 employees

Founded 2013

🎲 Gambling

🤝 B2B

☁️ SaaS

Gambling • B2B • SaaS

Gamingtec is a B2B iGaming platform provider that offers turnkey and white-label software for online casinos and sportsbooks. The company supplies modular, scalable platform solutions—casino, sportsbook (including an AI-powered sportsbook), affiliate software, and agent management—designed for rapid market entry (6–8 weeks), multi-jurisdiction regulatory compliance, and extensive integrations (game providers, payments, PAM). Gamingtec supports numerous markets and currencies, reports large event coverage (50,000+ monthly live events, 40,000+ monthly pre-match events), and promotes partnerships with payment and anti-fraud providers. It positions itself as a partner to operators seeking to launch or scale online gambling brands.

📋 Description

• Build and maintain infrastructure and tooling used to train, evaluate, deploy, and monitor ML models and GenAI services • Own production services, APIs, and pipelines powering 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 platform and engineering standards the company will rely on as it grows

🎯 Requirements

• 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 • Experience with MLOps workflows for model training, evaluation, deployment, and monitoring is advantageous • Experience serving ML models or LLM applications in production is advantageous • Experience with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms is advantageous • Experience with agent evaluation, prompt versioning, retrieval/search infrastructure, or vector-backed systems is advantageous • Experience supporting customer-facing APIs or SaaS platform infrastructure is advantageous

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