Engineering Manager, Applied AI & Machine Learning Engineering
New
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SPD TechnologyFinancial Technology
Ukraine. Poland. Spain. Portugal. Germany. Romania, Europe/KyivFull-TimeManager
Salary not disclosed
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Job Details
- Languages
- English (Upper Intermediate or higher), Ukrainian (fluent)
- Experience
- 7+ years in engineering or data science roles; 3+ years in an engineering leadership role
- Required Skills
- DockerPythonSQLApache AirflowCloud ComputingKubernetesMachine LearningPyTorchApache KafkaNLP
Requirements
- M.S. in Computer Science, Data Science, Machine Learning, Software Engineering, or related field.
- 7+ years in engineering or data science roles with a machine learning focus.
- 3+ years in an engineering leadership role with direct management of at least 5 people.
- 3+ years hands-on coding and delivering large-scale ML models and systems as a Machine Learning Engineer or Data Scientist.
- Deep expertise in natural language processing (NLP).
- Extensive experience with Python and associated ML libraries (pandas, scikit-learn, keras, PyTorch).
- Strong SQL knowledge.
- Experience owning 'as a service' ML models within large-scale distributed microservices architectures.
- Experience with data pipelines and platforms (e.g., Apache Kafka, Spark, Apache Airflow, AWS Glue, GCP Cloud Dataflow, Snowflake).
- Experience with containerization technologies such as Kubernetes and Docker.
- Fluent in Ukrainian; English proficiency at Upper Intermediate level or higher.
Responsibilities
- Lead machine learning engineering implementation, including operations, processes, practices, standards, and code quality.
- Ensure AI/ML roadmap items are delivered on-time and with exceptional quality.
- Serve as a force multiplier for AI/ML teams by removing roadblocks, implementing process improvements, and building practices for innovation.
- Maintain robust AI/ML engineering solutions and documentation, engaging with engineers at a code and design level.
- Describe technical context clearly for diverse audiences, from engineers to executive stakeholders.
- Mentor team members, solicit feedback, and foster a culture of belonging and psychological safety.
- Oversee the end-to-end lifecycle of AI/ML data systems from research and development to deployment.
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