Senior/Lead Machine Learning Engineer

We are looking for a capable Senior/Lead Machine Learning Engineer to join our remote team. The chosen candidate will have a significant role in the creation, development, and management of our ML pipeline, adopting industry-standard methodologies.
In this position, you will focus on building, deploying, maintaining, diagnosing, and improving components within the ML pipeline. Additionally, you will take the lead and contribute to the design and deployment of ML prediction endpoints. Partnering with System Engineers to establish the ML lifecycle management framework and advancing coding practices will be critical.
We welcome innovative individuals to become part of our dynamic team!
Responsibilities
Contribute to the design, development, and management of an ML pipeline aligned with best practices
Develop, deploy, maintain, troubleshoot, and improve ML pipeline components
Lead the design and deployment of ML prediction endpoints
Collaborate with System Engineers to create the ML lifecycle management framework
Write specifications, documentation, and user guides for applications
Improve coding practices and organize repositories within the scientific workflow
Set up pipelines for different projects
Identify technical risks and inconsistencies, proposing mitigation strategies
Work with data scientists to operationalize predictive models, ensuring clear understanding of model objectives and purposes, and develop scalable data preparation pipelines
Requirements
5+ years of programming experience, with a focus on Python and strong SQL knowledge
Proficiency in MLOps tools and frameworks (e.g., Sagemaker, Vertex, Azure ML)
Background in Data Science, Data Engineering, and DevOps Engineering at an intermediate level
Evidence of delivering at least one project in an MLE capacity
Expertise in Engineering Best Practices
Skills in utilizing the Apache Spark Ecosystem (Spark SQL, MLlib/SparkML) or equivalent technologies for Data Products
Familiarity with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.)
Proficiency in working with automated data pipeline and workflow management tools such as Airflow or Argo Workflow
Understanding of various data processing paradigms, including batch, micro-batch, and streaming
Experience with at least one major Cloud Provider, such as AWS, GCP, or Azure
Production familiarity with integrating ML models into complex, data-intensive systems
Knowledge of DS technologies such as Tensorflow, PyTorch, XGBoost, NumPy, SciPy, Scikit-learn, Pandas, Keras, Spacy, HuggingFace, Transformers
Competency in working with multiple database types, including Relational, NoSQL, Graph, Document, Columnar, Time Series, etc.
Nice to have
Background in Databricks MLOps-related tools or technologies, including MLFlow, Kubeflow, TensorFlow Extended (TFX)
Proficiency in performance testing tools, such as JMeter or LoadRunner
Understanding of containerization technologies like Docker