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We are seeking a skilled Senior Machine Learning Engineer to join our remote team. The successful applicant will play a substantial role in the design, development, and management of our ML pipeline, following industry-standard methodologies.
In this role, you will focus on constructing, deploying, maintaining, diagnosing, and enhancing steps within the ML pipeline. Furthermore, you will play a crucial role in leading and contributing to the design and deployment of ML prediction endpoints. Working in tandem with System Engineers to establish the ML lifecycle management environment and improve coding practices will be essential.
We invite those motivated by innovation to join our dynamic team!
- Contribution to the design, development, and management of an ML pipeline adhering to best practices
- Development, deployment, maintenance, troubleshooting, and enhancement of ML pipeline stages
- Leadership in designing and deploying ML prediction endpoints
- Collaboration with System Engineers to establish the ML lifecycle management setup
- Authoring specifications, documentation, and user guides for applications
- Enhancing coding practices and organizing repositories within the scientific workflow
- Configuring pipelines for various projects
- Continuous detection of technical risks and discrepancies, along with formulating mitigation plans
- Partnership with data scientists to operationalize predictive models, understanding the objectives and purposes of models developed by data scientists, and building scalable data preparation pipelines
- 3+ years of programming experience, ideally in Python, alongside robust SQL knowledge
- Profound MLOps experience (e.g., Sagemaker, Vertex, Azure ML)
- Intermediate proficiency in Data Science, Data Engineering, and DevOps Engineering
- Showcase of at least one project delivered to production in an MLE role
- Expertise in Engineering Best Practices
- Practical experience in implementing Data Products using Apache Spark Ecosystem (Spark SQL, MLlib/SparkML) or alternative technologies
- Familiarity with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.)
- Experience with automated data pipeline and workflow management tools such as Airflow, Argo Workflow, etc.
- Experience in different data processing paradigms such as batch, micro-batch, streaming
- Practical experience with at least one of the major Cloud Providers, including AWS, GCP, and Azure
- Production experience in integrating ML models into complex, data-driven systems
- Knowledge of DS using Tensorflow/PyTorch/XGBoost, NumPy, SciPy, Scikit-learn, Pandas, Keras, Spacy, HuggingFace, Transformers
- Experience with various types of databases including Relational, NoSQL, Graph, Document, Columnar, Time Series, etc.
- Practical experience with Databricks MLOps-related tools or technologies such as MLFlow, Kubeflow, TensorFlow Extended (TFX)
- Experience with performance testing tools like JMeter or LoadRunner
- Familiarity with containerization technologies like Docker
Find a vacancy that works for you. Send us your CV to receive a personalized offer.
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