Senior MLOps Platform Architect (AWS | Kubernetes | Terraform)

Senior MLOps Platform Architect (AWS | Kubernetes | Terraform) – Salve.Lab

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.

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

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 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

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.

Seniority level

Mid-Senior level

Employment type

Contract

Job function

Engineering and Information Technology

Industries

Technology, Information and Media

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