Senior MLOps Engineer – Data Platform
About the client and the project
Technology company building automation and machine learning platforms. An enterprise data platform built on a data mesh approach, where domain teams own their data as products and a central platform team provides governed, reusable, self-service capabilities. Machine Learning is the platform's next major capability, moving from models running outside the platform to a production-grade MLOps offering that domain teams can adopt independently.
What you will do
- Own the MLOps capability end to end, including its design, implementation quality and roadmap.
- Assess the existing data platform and refine the MLOps architecture to fit its governance, landing zones and catalog model.
- Design and deliver the target ML lifecycle incrementally, starting with one domain team and evolving it into a reusable path for others.
- Implement the full ML lifecycle including experiment tracking, model registry, feature tables, packaging, model serving and monitoring, tailored for regulated environments.
- Build controlled promotion pipelines across development, staging and production using CI/CD tooling, ensuring releases are reproducible and auditable.
- Build automated retraining and drift/skew detection, along with alerting and rollback mechanisms.
- Productionise batch and near real-time inference, and provide domain teams with cost visibility and observability for ML workloads.
Must-have skills
- MLOps, ML engineering or platform engineering — 5+ years
- Databricks — hands-on, deep expertise
- MLflow — hands-on, deep expertise
- Python — strong engineering skills
- Azure — solid experience
- Terraform — solid experience for ML infrastructure
- Experience with near real-time inference and streaming (Kafka, Spark Structured Streaming, Event Hubs or equivalent)
- Working understanding of data mesh principles
- English B2
Nice to have
- LLMOps: evaluation, prompt and version management, RAG, agent frameworks, token-cost management
- AWS
- Kubernetes for model serving
- ML monitoring tools such as Evidently, Arize or WhyLabs
- Experience managing and optimising ML workload costs with quantified results
- French
Location and work model
Why KWAN
At KWAN, your career is bigger than one project. You join a real team, on a long-term project, at a company where technology is the business. And when you are ready for something new — a new stack, a new industry, a bigger role — we find that next step with you, without you having to start over somewhere else.
From day one you have a dedicated People Experience Partner: one person accountable for your growth, not your allocation. They know where you want to go, and they help you get there.
You keep learning as technology moves (AI included). Your impact is seen and recognised. Pay and expectations are talked about openly. And you work remote-first, with hubs in Lisbon and Porto for when you want people around.
Grow with clarity. Be treated with respect. Build things that matter.
Apply — if your profile is a match for this role, you will hear from our Talent team within 48 hours. If it is not the right match this time, we will reach out as soon as we find a role that fits you better.
