Senior MLOps Platform Architect (AWS | Kubernetes | Terraform)

Salve.Inno Consulting; España; A distancia; EUR 50.000 - 70.000; Jornada completa

Descripción de la vacante

A leading consulting firm is hiring a Senior MLOps/DevOps/SRE hybrid to build an end-to-end AI platform infrastructure. You'll design and develop AWS-based infrastructure and Kubernetes platform while ensuring CI/CD and observability for AI models. Ideal candidates will have extensive experience in production-grade MLOps environments, Kubernetes management, and Terraform. This is a remote-first position offering competitive compensation and 20+ days of paid time off.

Servicios

Competitive compensation

20+ days paid time off

Training & development budget

Apple gear

Formación

• 5+ years in Senior DevOps, SRE, or MLOps Engineering role.
• Strong experience designing and building Kubernetes clusters in production.
• Hands-on expertise with Terraform or similar IaC tools.

Responsabilidades

• Design and build AWS-based AI/ML infrastructure using Terraform.
• Architect and operate production Kubernetes clusters.
• Build automated CI/CD pipelines for ML workloads.

Conocimientos

Kubernetes management

Terraform

Python or Go programming

CI/CD pipeline creation

Machine Learning deployments

Herramientas

Docker

GitLab

Jenkins

MLflow

Kubeflow

Prometheus

Grafana

Descripción del empleo

Remote | B2B Contract | Europe (PL/ES/PT/CZ/CY)

Role Overview

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.

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.

Requirements:

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’s on Offer

• Competitive fixed compensation based on experience and expertise.
• Work on cutting‑edge AI systems used globally.
• 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.

Diversity and Inclusion Commitment

We are dedicated to creating and sustaining an inclusive, respectful workplace for all – regardless of gender, ethnicity, or background. We actively encourage applicants from all identities and experience levels to apply and bring your authentic self to our fast‑paced, supportive team.