Senior MLOps Engineer @

3 days ago

Remote Kraków, Czech Republic Massive Rocket Full time

Required Skills and Qualifications:

- 5+ years of experience in MLOps, DevOps, or Data Engineering with a strong focus on ML model deployment.
- Strong expertise in ML lifecycle automation, model versioning, and monitoring.
- Hands-on experience with ML pipeline orchestration tools such as Apache Airflow, Kubeflow, or MLflow.
- Proficiency in programming languages such as Python (Pandas, NumPy, TensorFlow/PyTorch) & SQL.
- Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for ML.
- Familiarity with cloud platforms (Azure preferred, AWS or GCP also considered).
- Experience with infrastructure as code (Terraform, CloudFormation).
- Strong understanding of data engineering concepts and working with large-scale data processing frameworks (Spark, Databricks).
- Familiarity with version control systems (e.g., Git) and collaborative development workflows.
- Excellent problem-solving skills and ability to work in a fast-paced environment.
- Strong communication skills (English C1 level) and experience working in client-facing roles.

Bonus Skills and Experiences:

- Experience with real-time model serving using tools like TensorFlow Serving, TorchServe, or Triton Inference Server.
- Hands-on experience with feature stores like Feast or Tecton.
- Experience working with compliance-driven industries (e.g., finance, healthcare, regulated environments).
- Knowledge of graph databases, NLP models, or reinforcement learning workflowsemonstrated experience with Terraform for infrastructure provisioning and management.

Desired Qualities:
- Innovative Problem-Solver: A creative thinker who can efficiently solve complex problems and adapt to new technologies and changing product requirements.
- Quality Advocate: Passion for quality and a dedication to understanding the user's perspective and how it impacts the product's overall experience.
- Effective Communicator: Strong interpersonal and communication skills, with the ability to articulate issues, solutions, and concepts to technical and non-technical stakeholders alike.
- Leadership Potential: While direct leadership experience is not mandatory, the aptitude to mentor others and lead by example in software engineering practices is highly valued.

Massive Rocket is a high-growth Braze & Snowflake agency that has made significant strides in connecting digital marketing teams with product and engineering units. Founded just 5 years ago, we have experienced swift growth and are now at a crucial juncture, aspiring to reach $100M in revenue. 
Our focus is on delivering human experiences at scale, leveraging the latest in web, mobile, cloud, data, and AI technologies. We pride ourselves on innovation and the delivery of cutting-edge digital solutions.

Every role at Massive Rocket is Entrepreneurial - Successful people at Massive Rocket will not only think about their role but understand the roles around them, their goals, and contribute to the success and growth of their team, customers, and partners.
What We Offer Fast-moving environment – you will never stop learning and growing Supportive and positive work culture with an emphasis on our values International presence – work with team members in Europe, the US, and around the globe 100% remote forever Flexible Vacation Policy Career progression paths and opportunities for promotion/advancement Organised team events and outings

What we're looking forMassive Rocket, a global Martech agency specializing in Braze and Snowflake, is looking for a talented Senior MLOps Engineer to join our growing team. We work with clients across the U.S., U.K., and European Union, delivering cutting-edge AI and machine learning solutions.We are seeking a highly skilled and motivated Senior MLOps Engineer to design, implement, and optimize robust, scalable, and efficient machine learning infrastructure. You will work closely with our data scientists, ML engineers, and product teams to streamline model deployment, monitoring, and operationalization, ensuring reliable and scalable AI-driven solutions.

During the process, please be ready to provide:

  • Valid work visa - Massive Rocket does not provide sponsorship at the moment.
  • Proof of identification: ID card, passport, Utility bill (Gas, Water, Electricity)
  • 2 references - Name, Relationship, Contact details (Email, Mobile)
  • Contractors Only: proof of incorporation and insurance
    Note: Please ensure that your qualifications closely match the criteria outlined in the job description. Applications not meeting the specified criteria may not be processed or considered for this position.
,[ML Model Deployment & Operationalization:Automate and streamline the deployment of ML models to production environments.Implement CI/CD pipelines for ML models, ensuring efficient retraining and deployment.Optimize model inference performance and scalability., Infrastructure & Cloud Deployment:Design and manage scalable cloud-based ML infrastructure (Azure, AWS, or GCP).Utilize Kubernetes and Docker for containerized ML model deployment.Optimize cloud costs while maintaining high availability and performance., Data Pipeline Orchestration & Feature Engineering:Build and maintain scalable ETL/ELT data pipelines for ML model training.Automate data ingestion, transformation, and feature engineering processes.Leverage Apache Airflow or similar tools to schedule and monitor data workflows., Monitoring & Performance Optimization:Implement monitoring solutions to track model performance, drift, and anomalies.Automate logging, alerting, and retraining mechanisms to improve model robustness.Ensure compliance with security and governance standards., Infrastructure as Code (IaC) & Automation:Use Terraform or similar tools to automate the provisioning of cloud infrastructure.Define reusable, modular configurations to support scalable deployment., Collaboration & Documentation:Work closely with data scientists, software engineers, and DevOps teams to improve workflows.Maintain comprehensive documentation of ML infrastructure, processes, and best practices.] Requirements: MLOps, AzureML, MLFlow, Snowflake, dbt, Airflow, Terraform, LLM, Kubernetes

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