Founding AI Platform Engineer – MLOps, Backend

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🔥 44 minutes ago

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Gamingtec

WebsiteLinkedInAll Job Openings

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 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.

🎯 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 (advantage) • Experience serving ML models or LLM applications in production (advantage) • Experience with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms (advantage) • Experience with agent evaluation, prompt versioning, retrieval/search infrastructure, or vector-backed systems (advantage) • Experience supporting customer-facing APIs or SaaS platform infrastructure (advantage).