Customer Facing MLOps Engineer

Skills & Requirements

Technical Skills

a. Machine Learning Operations (MLOps) Skills

• Model Deployment and Serving: Experience with deploying machine learning models in production environments. Proficiency with tools like Docker, Kubernetes, TensorFlow Serving, TorchServe, and MLflow.
• CI/CD for ML: Knowledge of setting up the (CI/CD) framework and practices tailored for ML projects. Familiarity with tools like Jenkins, GitLab CI, and CircleCI.
• Monitoring and Logging: Skills in monitoring model performance and logging, using tools such as Prometheus, Grafana, and ELK Stack (Elasticsearch, Logstash, Kibana).
• Model Versioning and Governance: Experience with model version control, reproducibility, and compliance, using platforms like DVC (Data Version Control) and MLflow.
• Automated Testing: Knowledge of automated testing frameworks for ML, including unit tests, integration tests, and model validation.

b. Data Engineering Skills

• Data Pipeline Development: Proficiency in building and maintaining scalable data pipelines using tools like Apache Airflow, Apache NiFi, or Luigi.
• ETL (Extract, Transform, Load) Processes: Strong understanding of ETL processes and tools such as Apache Spark, Hadoop, Talend, or Informatica.
• Database Management: Experience with both relational (SQL) and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra). Ability to design and optimize database schemas.
• Data Warehousing: Familiarity with data warehousing solutions like Snowflake or Azure Synapse.
• Data Cleaning and Preprocessing: Skills in data wrangling, cleaning, and preprocessing using Python (Pandas, NumPy) or R.
• Data Security and Governance: Knowledge of data security practices, GDPR, and data governance tools and frameworks.

c. Programming and Scripting

• Programming Languages: Proficiency in Python and SQL. Knowledge of Java, Scala, or R can be beneficial.
• Scripting: Experience with shell scripting (Bash) and automation scripts to streamline workflows.

2. Cloud Computing and Infrastructure

• Cloud Platforms: Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP) for deploying and managing ML models and data pipelines.
• Infrastructure as Code (IaC): Familiarity with IaC tools like Terraform, AWS CloudFormation, or Ansible for automating infrastructure deployment.
• Containerization and Orchestration: Proficiency with Docker and container orchestration tools like Kubernetes for scalable deployments.

We offer you:

• Meaningful work in purpose driven organisation, Health for life
• Great team spirit and fun colleagues from Sweden and Finland
• Interesting forward leaning organisation with high ambitious for sustainability
• Organisation which gives independency and responsibility to develop Organisation with Scandinavian view and some travelling
• Interesting business in the middle of the health industry from B2B service business to B2C customer relations.

Terms of Employment:

• This is a permanent full-time position, and we want you to start asap
• This position is based in Stockholm, Espoo or Gothenburg
• The Employment is within the Oriola Group – below you can read about our great company

Application: We will start recruitment interviews with the applicable candidates already during the application period. We hope to hear from you soon!