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Текст:
Hirify AI/Оригинал
TL;DR
Founding AI Platform Engineer (MLOps / Backend) (AI/MLOps): Building the production infrastructure, services, and tooling that train, evaluate, deploy, serve, and monitor ML models and GenAI systems with an accent on reliability, scalability, observability, and seamless deployment. Focus on designing reproducible model and prompt lifecycles, improving CI/CD and rollback workflows, and solving complex platform, performance, security, and cost challenges.
Location: Remote from London, Limassol, Tbilisi, or Yerevan
Company
hirify.global is hiring for an AI platform engineering role focused on production ML and GenAI systems.
What you will do
Build and maintain infrastructure and tooling for training, evaluating, deploying, serving, and monitoring ML models and GenAI services.
Own production services, APIs, and pipelines supporting recommendations, agent workflows, and customer-facing integrations.
Improve CI/CD, testing, release, rollback, and environment-management workflows.
Establish observability for service health, model behavior, agent quality, latency, cost, and failure modes.
Build reproducibility and lifecycle practices for models, prompts, datasets, configurations, and releases.
Improve experimentation, reliability, scalability, security, performance, and cost efficiency across the platform.
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 regarding 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.
Nice to have
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 or search infrastructure, or vector-backed systems.
Experience supporting customer-facing APIs or SaaS platform infrastructure.
Culture & Benefits
Remote work from London, Limassol, Tbilisi, or Yerevan.
Unlimited holidays and medical insurance.
Challenging projects and tasks with ambitious goals.
Work environment emphasizing diversity, innovation, and collaboration with experienced colleagues.
Hiring process
30-minute interview with an HR team member.
One-hour technical interview.
Final interview focused on culture and working style.
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