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Senior ML Platform Engineer (MLOps) at CO-WORKER TECHNOLOGY
Norrtälje kommun, , Finland -
Full Time

Start Date

Immediate

Expiry Date

02 Feb, 26

Salary

0.0

Posted On

04 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, MLOps, AWS, Azure, GCP, Python, Kubernetes, Containers, Terraform, Airflow, MLflow, SageMaker, CI/CD, Data Quality, Model Monitoring, Security

Industry

Staffing and Recruiting

Description
The role Design, build and operate an ML platform (training & inference) on AWS/Azure/GCP. Automate the model lifecycle: ingestion, feature store, experiment tracking, CI/CD, model registry, approvals and rollbacks. Establish reliable deployment patterns (batch, streaming, online inference; containers/Kubernetes). Implement observability for data and model quality, drift, latency, cost, and reliability (SLOs/alerting). Contribute to platform security, governance and compliance (secrets, PII, access, lineage). What you bring MSc/BSc in Computer Science, Data/Software Engineering or similar. 5+ years building production data/ML systems (MLOps/Platform/SRE background). Strong Python and at least one cloud (AWS/Azure/GCP). Hands-on with Kubernetes/containers and IaC (Terraform or similar). Practical experience with the ML toolchain (e.g., Airflow/Prefect, feature stores, MLflow, SageMaker/Vertex/Azure ML, Spark/Ray, vector DBs/RAG). CI/CD for ML (GitHub/GitLab/Azure DevOps), testing strategies, blue/green or canary releases. Solid understanding of data quality, drift detection and model monitoring in production. Professional English; Swedish or Finnish is a plus. Nice to have Experience enabling GenAI/LLM use cases (prompting/RAG pipelines, embeddings, guardrails). FinOps/cost engineering for ML workloads. Security-by-design for ML (secret management, least-privilege, audit, privacy). Background with high-throughput or low-latency systems.
Responsibilities
Design, build and operate an ML platform for training and inference on cloud platforms. Automate the model lifecycle and establish reliable deployment patterns while implementing observability for data and model quality.