Senior MLOps Platform Architect

Salve.Inno Consulting; Deutschland; Remote; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading AI consultancy in Germany seeks a Senior MLOps professional to design and manage its AI platform infrastructure. The role involves building AWS-based systems, operating Kubernetes clusters, and implementing CI/CD pipelines for machine learning models. Candidates should have extensive experience with MLOps, Kubernetes, and Terraform. This position offers competitive compensation, remote work, and opportunities to work on advanced AI technologies.

Leistungen

Competitive fixed compensation

20+ days paid time off

Apple gear

Training & development budget

Qualifikationen

• 5+ years in a Senior DevOps or MLOps Engineering role supporting production environments.
• Strong experience with Kubernetes clusters in production.
• Hands-on expertise with Terraform for cloud infrastructure.
• Solid programming skills in Python or Go.

Aufgaben

• Design and build AWS-based AI/ML infrastructure using Terraform.
• Architect, build, and operate production Kubernetes clusters.
• Build automated training, validation, and deployment pipelines.
• Implement full observability using Prometheus and Grafana.

Kenntnisse

AWS infrastructure management

Kubernetes

Terraform

Python

CI/CD pipelines

ML workflows

Tools

GitLab

Docker

Prometheus

Grafana

Kubeflow

MLflow

Jobbeschreibung

Remote | B2B Contract | Europe

Role Overview

We are hiring a senior MLOps 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.