ML Ops Engineer

About us:

Our mission is to supercharge IT enterprise organizations with custom AI solutions. Headquartered in the heart of Munich with a hub in Lisbon, we’re a team of builders, dreamers, and problem-solvers tackling some of the most exciting and complex challenges in AI native development adoption.

We operate on speed , adaptability , and extreme ownership . That means we move fast, stay flexible, and take full responsibility for our impact. Our clients trust us because we don’t ship generic tools — we embed AI into real-world enterprise workflows with precision and empathy.

What You’ll Do:

As a ML Ops Engineer, you’ll be the backbone of our technical infrastructure — owning the delivery pipeline , model serving stack , and runtime systems that power our AI workflows.

Your mission: ensure that our AI agents, APIs, and web interfaces are reliable, scalable, and fast , from prototype to production.

• Build and maintain backend APIs and services to support AI-driven features and workflows.

• Own our ML Ops pipeline: from model versioning and testing to containerized deployment and CI/CD.

• Set up observability and monitoring for LLM-based services and agentic systems.

• Manage infrastructure for fine-tuning, retrieval-augmented generation (RAG), and real-time agent orchestration.

• Collaborate closely with AI engineers to streamline model integration, scaling, and latency optimization.

• Contribute to frontend features and internal tooling as needed — you’re not afraid of building end-to-end.

• Automate everything you can — and keep our infrastructure lean, secure, and maintainable.

What We’re Looking For:

You’re a ML Ops Engineer who’s not afraid to get deep into infra. You understand what it takes to ship AI-powered features to production , and you take pride in building systems that just work.

Must-Haves:

• Experience deploying LLMs or RAG pipelines in production.

• Strong experience with Docker, CI/CD pipelines (GitHub Actions, GitLab CI, etc.), and cloud infrastructure (AWS, GCP, or Azure).

• Solid understanding of ML Ops workflows, including model packaging, deployment, and serving.

• Experience with logging, tracing, monitoring, and metrics (Prometheus, Grafana, Sentry, etc.).

• High autonomy — you’re comfortable owning infra and deployment from day one.

Bonus Points:

• 3+ years experience in full stack or backend engineering roles.

• Proficiency with Python (FastAPI, Flask, or similar) and modern JS frameworks (React, Next.js, etc.).

• Familiarity with GPU orchestration and efficient serving (Triton, vLLM, or Hugging Face Inference).

• Exposure to enterprise authentication and compliance requirements.

• Contributions to internal tooling for ML teams (feature stores, model registries, sandbox environments, etc.).

• Knowledge of data engineering best practices (ETL pipelines, batch vs. streaming).

Why Join Us:

• Be the bridge between cutting-edge AI and rock-solid production systems.

• Work closely with a team of AI engineers, product thinkers, and enterprise clients.

• Build infrastructure that powers agents, not just dashboards.

• Enjoy a high-trust, fast-moving culture where you can take real ownership of what you build.

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