Cloud Solutions Architect

Your Strategic Partner for HR, Payroll & Headhunting Solutions

We are hiring a senior MLOps / DevOps / SRE hybrid who can build an entire AI platform infrastructure end-to-end. This is not a research role and not a standard ML Engineer role. If you haven't designed production-grade MLOps infrastructure, haven't built CI / CD for ML, or haven't deployed ML workloads on Kubernetes at scale, this role is not a fit.

You will design, build, and own the AWS-based infrastructure, Kubernetes platform, CI / CD pipelines, and observability stack that supports our AI models (Agentic AI, NLU, ASR, Voice Biometrics, TTS). You will be the technical owner of MLOps infrastructure decisions, patterns, and standards.

Location : Remote - Europe (PL / ES / PT / CZ / CY)

Key Responsibilities

• MLOps Platform Architecture (from scratch)
• Design and build AWS-based AI / ML infrastructure using Terraform (required) .
• Define standards for security, automation, cost efficiency, and governance.
• Architect infrastructure for ML workloads, GPU / accelerators, scaling, and high availability.
• Kubernetes & Model Deployment
• Architect, build, and operate production Kubernetes clusters.
• Containerize and productize ML models (Docker, Helm).
• Deploy latency-sensitive and high-throughput models (ASR / TTS / NLU / Agentic AI).
• Ensure GPU and accelerator nodes are properly integrated and optimized.
• CI / CD for Machine Learning
• Build automated training, validation, and deployment pipelines (GitLab / Jenkins).
• Implement canary, blue-green, and automated rollback strategies.
• Integrate MLOps lifecycle tools (MLflow, Kubeflow, SageMaker Model Registry, etc.).
• Observability & Reliability
• Implement full observability (Prometheus + Grafana).
• Own uptime, performance, and reliability for ML production services.
• Establish monitoring for latency, drift, model health, and infrastructure health.
• Collaboration & Technical Leadership
• Work closely with ML engineers, researchers, and data scientists.
• Translate experimental models into production-ready deployments.
• Define best practices for MLOps across the company.

Qualifications and Skills

We're looking for a senior engineer with a strong DevOps / SRE background who has worked extensively with ML systems in production. The ideal candidate brings a combination of infrastructure, automation, and hands-on MLOps experience.

Ways to Know You'll Succeed

What we offer

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