ML Engineer
Ework Group
This role is more about ML Operations (MLOps) and Data Engineering than pure ML research. You will be productionizing ML models, building pipelines, automating deployments, and maintaining infrastructure on GCP/Azure. The position is through an agency (Ework Group) for a client, likely in the energy or industrial sector (oilfield terminology mentioned). Expect to work on ETL, CI/CD, containerization (Docker/K8s), and sometimes give talks.
Brakuje: client name and industry (oilfield terminology suggests energy, but not confirmed), team size and structure.
The title suggests an ML Engineer focused on model development, but the daily work is heavily MLOps and data engineering: building pipelines, automating deployments, and handling infrastructure. You will rarely build new ML models; instead, you'll operationalize existing ones.
This role is more about ML Operations (MLOps) and Data Engineering than pure ML research. You will be productionizing ML models, building pipelines, automating deployments, and maintaining infrastructure on GCP/Azure. The position is through an agency (Ework Group) for a client, likely in the energy or industrial sector (oilfield terminology mentioned). Expect to work on ETL, CI/CD, containerization (Docker/K8s), and sometimes give talks.
- −Agency model (Ework Group is a staffing agency) – you will be a contractor assigned to an unspecified client, which may lack long-term stability.
- −On-site only in Warsaw – no remote flexibility mentioned.
- −Wide range of required technologies (many DL frameworks) which may not all be used daily.
- −No mention of on-call or overtime compensation.
- !Client details not provided – unknown which company or project.
- !Team size and composition not specified.
- !No information about the recruitment process (stages, timeline, etc.).
- !B2B contract without mention of additional benefits.
- •Writing production-ready Python code for ML inference and data processing
- •Leveraging GPU/CPU resources and managing capacity for ML workloads
- •Architecting and automating MLOps pipelines on GCP/Azure
- •Designing reusable tools for monitoring, optimizing, and maintaining ML solutions
- •Building and maintaining ETL workflows using Airflow or similar orchestration tools
- •Implementing CI/CD pipelines and managing Git-based workflows
- •Deploying and monitoring RESTful APIs for ML models
- •Conducting internal workshops and participating in external conferences
Oferta skierowana do developerów z doświadczeniem komercyjnym (Mid).
An ML Engineer with at least 3 years of experience, proficiency in Python, some cloud exposure (GCP or Azure), basic Docker/Kubernetes, and willingness to focus on MLOps and data pipelines rather than model development.
Not for junior candidates with less than 2 years of experience, nor for pure-ML researchers who do not want to handle infrastructure, pipelines, and DevOps tasks.
- ?For which client is this position – industry and project specifics?
- ?How many people are in the ML/Data team?
- ?Is there an on-call rotation? If so, how often?
- ?What is the expected duration of the contract?
- ?Will I work on a single project or rotate between clients?
- ?Are there opportunities for conference attendance or training?
- ?What is the exact recruitment process (number of interviews, technical task)?
- −Client name and industry (oilfield terminology suggests energy, but not confirmed)
- −Team size and structure
- −On-call requirements
- −Recruitment process (stages, timeline)
- −Benefits on B2B (e.g., paid leave, training budget)
- −Specific ML models or use cases in production
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