Senior Machine Learning Engineer
We are seeking an experienced Senior Machine Learning Engineer to join our team. The ideal candidate will take on leading roles in designing, developing, and optimizing our machine-learning platform. Your contributions will drive the success of our prediction models in real-world applications.
Responsibilities
- Contribute to the design, development, and operational lifecycle of the ML pipeline based on best practices
- Design, create, maintain, troubleshoot, and optimize ML pipeline steps
- Own and contribute to the design and implementation of ML prediction endpoints
- Collaborate with System Engineers to configure the ML lifecycle management environment
- Write specifications, documentation, and user guides for developed applications
- Promote improved coding practices and repository organization in the science work cycle
- Establish and configure pipelines for projects
- Identify technical risks and gaps, and devise mitigation strategies
- Collaborate with data scientists to productionalize predictive models, understand the scope and purpose of the models built by data scientists, and create scalable data preparation pipelines
Requirements
- Minimum of 3 years of experience in programming languages, ideally Python, with strong knowledge of SQL
- Robust experience in MLOps (e.g., SageMaker, Vertex AI, or Azure ML)
- Intermediate-level expertise in Data Science, Data Engineering, and DevOps Engineering
- Experience delivering at least one project to production in an MLE role
- Expertise in engineering best practices
- Practical experience implementing data products using the Apache Spark Ecosystem (e.g., Spark SQL, MLlib/SparkML) or alternative technologies
- Experience with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.)
- Proficiency in automated data pipeline and workflow management tools (e.g., Airflow, Argo Workflows, etc.)
- Experience with various data processing paradigms (batch, micro-batch, streaming)
- Practical experience working with at least one major cloud provider (e.g., AWS, GCP, or Azure)
- Production experience integrating ML models into complex data-driven systems
- Data science experience with tools and frameworks such as TensorFlow, PyTorch, XGBoost, NumPy, SciPy, scikit-learn, pandas, Keras, SpaCy, Hugging Face, and Transformers
- Experience working with different types of databases (e.g., Relational, NoSQL, Graph, Document, Columnar, Time Series, etc.)
- Fluency in English, both written and spoken, at a minimum B2 level
