ML Ops Engineer

About the role

Zeta Global is seeking an experienced ML Ops Engineer with more than three years of professional experience in software development or applied machine learning. This role is focused on designing, building, and maintaining scalable machine learning solutions within a cloud environment, primarily leveraging AWS services. The position involves working at the intersection of data science and engineering, requiring a strong understanding of model development, experimentation, deployment, and monitoring. The ideal candidate will be responsible for ensuring that machine learning models are effectively integrated into production systems, optimized for performance, and maintained over time to meet evolving business needs. The role offers an to work on real-world problems, collaborate with cross-functional teams, and contribute to the company's data-driven initiatives.

Key facts

What you'll do

• Design, develop, and implement machine learning models to address complex business challenges, ensuring solutions are scalable and maintainable.
• Conduct comprehensive data exploration, feature engineering, and experimentation to validate model effectiveness and robustness.
• Develop and automate workflows that facilitate reproducibility, version control, and reliable deployment of machine learning models.
• Collaborate closely with data scientists, data engineers, software engineers, and product managers to integrate ML models seamlessly into production environments.
• Monitor the performance of deployed models continuously, analyze metrics, and implement improvements based on feedback and changing data patterns.
• Automate the training, testing, and deployment processes to streamline model lifecycle management and reduce manual intervention.
• Maintain detailed documentation of models, pipelines, experiments, and deployment procedures to ensure transparency and reproducibility.
• Ensure compliance with data privacy, security standards, and ITAR regulations, especially when handling sensitive data.
• Optimize models for efficiency, speed, and resource utilization in cloud environments, particularly AWS.
• Transition prototypes and experimental models into production-ready systems, including packaging, testing, and deployment pipelines.
• Develop and maintain APIs or lightweight services that enable real-time or batch inference for integrated applications.
• Collaborate on establishing best practices for model versioning, experiment tracking, and reproducibility across teams.
• Stay informed about the latest developments in machine learning, cloud computing, and deployment techniques to continuously improve processes and solutions.
• Participate in code reviews, knowledge sharing, and team meetings to foster a collaborative and innovative work environment.
• Contribute to the development of internal tools and frameworks that enhance ML operations capabilities.
• Support the scaling of ML solutions as the company's data and model complexity grow, ensuring systems remain reliable and performant.

Requirements

• Minimum of three years of experience in software development, data science, or applied machine learning roles.
• Deep understanding of machine learning algorithms, statistical analysis, and experimental design principles.
• Proven track record of building, deploying, and maintaining machine learning models that solve practical business problems.
• Strong experience working with both structured and unstructured data, including feature engineering, data preprocessing, and dataset management.
• Ability to evaluate models using appropriate metrics, perform model selection, and understand trade-offs between different approaches.
• Hands-on experience deploying machine learning solutions in cloud environments, especially AWS, including services such as EC2, S3, Lambda, and SageMaker.
• Proficiency in Python programming, with an emphasis on writing clean, efficient, and maintainable code.
• Experience with transitioning prototypes into production systems, including packaging models, creating deployment pipelines, and monitoring performance.
• Familiarity with containerization tools like Docker and orchestration frameworks such as Kubernetes is advantageous.
• Knowledge of CI/CD practices to automate testing, deployment, and updates of ML models and pipelines.
• Strong communication skills in English, both written and verbal, to collaborate effectively across teams and document work clearly.
• A Master's degree or higher in Computer Science, Mathematics, Physics, Statistics, or a related quantitative field, or equivalent practical experience.
• Ability to work in a fast-paced environment, manage multiple priorities, and adapt to changing requirements.

Nice to have

• Experience with popular ML frameworks such as scikit-learn, TensorFlow, PyTorch, or XGBoost.
• Familiarity with ML experiment tracking tools like MLflow, Weights & Biases, or similar platforms to ensure reproducibility.
• Knowledge of SQL, data warehouses, data lakes, and tools such as Apache Airflow, dbt, or Spark for data processing and orchestration.
• Exposure to feature stores, embedding pipelines, or vector search systems to enhance model performance and scalability.
• Experience developing APIs or lightweight services to serve models in real-time or batch inference scenarios.
• Basic understanding of Docker, orchestration tools, and CI/CD pipelines to streamline deployment workflows.
• Experience working within agile, remote, or asynchronous team environments, demonstrating flexibility and collaboration skills.
• Contributions to open-source ML projects, research publications, or participation in machine learning competitions.

Skills & tools

• Python programming language
• AWS cloud platform and related services (EC2, S3, Lambda, SageMaker)
• Machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or XGBoost
• SQL and data management tools
• Docker containerization
• CI/CD tools and practices for automation and deployment

Practical notes

Zeta Global values diversity and encourages applications from candidates of all backgrounds. The company is committed to fostering an inclusive workplace culture where everyone can thrive. They offer competitive compensation packages, including stock options, and prioritize work-life balance through flexible working hours and remote work options. The role involves on-site work at the Prague office, providing opportunities for collaboration and team engagement. Zeta Global emphasizes continuous learning and development, supporting employees in staying current with technological advancements in machine learning and cloud deployment. The company also promotes a collaborative environment where knowledge sharing and innovation are encouraged, ensuring that team members grow professionally while contributing to impactful projects.

About the company

 Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI.