Senior MLOps Platform Architect (AWS | Kubernetes | Terraform)

theHRchapter; Málaga; A distancia; EUR 50.000 - 70.000; Jornada completa

Descripción de la vacante

A leading HR solutions provider is seeking a Senior MLOps / DevOps engineer to design and build the infrastructure for AI platforms. The ideal candidate will have over 5 years of experience, particularly with AWS, Kubernetes, and CI/CD pipelines, and will work in a dynamic remote-first environment, focusing on innovative AI solutions.

Servicios

Competitive fixed compensation

20+ days paid time off

Apple gear

Training & development budget

Formación

• 5+ years in a Senior DevOps, SRE, or MLOps Engineering role.
• Strong experience with Kubernetes in production.
• Hands-on expertise with Terraform for cloud infrastructure.

Responsabilidades

• Design and build AWS-based AI/ML infrastructure.
• Architect and operate production Kubernetes clusters.
• Build automated training and deployment pipelines.

Conocimientos

AWS infrastructure management

Kubernetes cluster management

Terraform

CI / CD pipeline development

Python programming

ML model deployment

Herramientas

GitLab

Jenkins

Docker

Helm

MLflow

Grafana

Prometheus

Descripción del empleo

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.

• 5+ years in a Senior DevOps, SRE, or MLOps Engineering role supporting production environments.
• Strong experience designing, building, and maintaining Kubernetes clusters in production.
• Hands-on expertise with Terraform (or similar IaC tools) to manage cloud infrastructure.
• Solid programming skills in Python or Go for building automation, tooling, and ML workflows.
• Proven experience creating and maintaining CI / CD pipelines (GitLab or Jenkins).
• Practical experience deploying and supporting ML models in production (e.g., ASR, TTS, NLU, LLM / Agentic AI).
• Familiarity with ML workflow orchestration tools such as Kubeflow , Apache Airflow , or similar.
• Experience with experiment tracking and model registry tools (e.g., MLflow , SageMaker Model Registry ).
• Exposure to deploying models on GPU or specialized hardware (e.g., Inferentia , Trainium ).
• Solid understanding of cloud infrastructure on AWS , including networking, scaling, storage, and security best practices.
• Experience with deployment tooling (Docker, Helm) and observability stacks (Prometheus, Grafana).

Ways to Know You’ll Succeed

• You enjoy building platforms from the ground up and owning technical decisions.
• You’re comfortable collaborating with ML engineers, researchers, and software teams to turn research into stable production systems.
• You like solving performance, automation, and reliability challenges in distributed systems.
• You bring a structured, pragmatic, and scalable approach to infrastructure design.
• Energetic and proactive individual, with a natural drive to take initiative and move things forward.
• Enjoys working closely with people - researchers, ML engineers, cloud architects, product teams.
• Comfortable sharing ideas openly, challenging assumptions, and contributing to technical discussions.
• Collaborative mindset : you like to build together, not work in isolation.
• Strong ownership mentality - you enjoy taking responsibility for systems end-to-end.
• Curious, hands-on, and motivated by solving complex technical challenges.
• Clear communicator who can translate technical work into practical recommendations.
• Thrives in a fast-paced environment where you can experiment, improve, and shape how things are done.

What we offer

• Competitive fixed compensation based on experience and expertise.
• Work on cutting-edge AI systems used globall.
• Dynamic, multi-disciplinary teams engaged in digital transformation.
• Remote-first work model
• Long-term B2B contract
• 20+ days paid time off
• Apple gear
• Training & development budget

Our Core values at TheHRchapter

• ️ Transparency : We believe in transparent and smooth recruitment processes. You will get feedback from us.
• ️ Candidate experience : Perfect blend between automated and humanized recruitment processes. Don't hesitate to ask us for feedback, anytime.
• ️ Talented pool : We bring highly-skilled motivated candidates to our clients. Our candidates match their company values and management style.
• ️ Diversity and inclusion : There is no place for discrimination and intolerance. We care about diversity awareness and respect for any differences.