Cloud Machine Learning Engineer (Remote, U.S.)

About: Onix is a leading cloud consulting company that helps customers get the most out of their technology with innovative, cloud-powered solutions and best-in-class services. We offer cloud solutions across various use cases and industries, from migration to advanced analytics to innovative AI solutions. With our global team of accomplished cloud experts, we are the most trusted cloud partner in the industry.

Summary: Onix seeks an experienced Cloud Machine Learning Engineer, who will be vital in designing and implementing solutions that meet client business needs and support the overall cloud architecture. As a Cloud Machine Learning Engineer, you will be critical in defining the technical architecture, selecting appropriate technologies and developing Machine Learning models and systems. You will work closely with cross-functional teams, including data scientists, engineers, architects and business stakeholders in order to deliver innovative and scalable machine-learning solutions that address complex business challenges.

Location: Remote (U.S.)

Primary Responsibilities: 

• Lead the design, development and implementation of enterprise-level Machine Learning solutions, from data collection and preprocessing to model training, evaluation and deployment that integrate into the overall data architecture
• Provide thought leadership and technical expertise in designing, building and optimizing end-to-end data pipelines, from data extraction and transformation to loading and visualization
• Collaborate with business stakeholders, data engineers, data scientists and other teams to understand data requirements, use cases and business objectives
• Architect scalable and efficient Machine Learning pipelines and workflows, leveraging best practices and cutting-edge technologies that incorporate into Databricks
• Design and implement data processing and feature engineering pipelines to extract relevant information from diverse, large-scale datasets
• Ensure data quality, integrity and governance standards are met throughout the data engineering lifecycle
• Create and maintain technical documentation, run books, code and best practices for data and machine learning engineering on the Databricks platform
• Perform various functions related to additional technologies like Spark/Python/PySpark, R, etc.
• Provide client presentations to review project design, outcomes and recommendations

Qualifications & Skills:

• 5+ years of consulting experience, working with external clients across a variety of different industries/markets
• 7+ years experience in data architecture, data engineering, data science and analytics in areas such as performance optimization, pipeline integration, infrastructure configuration, etc.
• 3+ years of relevant experience developing production-level PySpark/Python code
• 3+ years of relevant experience developing production-level R/sparklyr code
• Experience in developing reusable tooling (libraries, modules, etc.) using object-oriented design to manage code complexity alongside willingness to learn object-oriented design principles, as necessary
• Experience in and familiarity with MLOps principles, including, but not limited to, model registries, model serving (batch and real-time), model versioning, promotion, model and feature drift, and automated re-training
• In-depth knowledge of AWS data services and related technologies, including but not limited to, Athena, Redshift, Glue, S3, Lambda, SageMaker, Amazon Managed Workflows for Apache Airflow (MWAA), etc.
• Deep knowledge and expertise in Databricks and its components, such as Unity Catalogs, Delta Lakes, Delta Live Tables, Apache Spark (and PySpark), Lakehouse Monitoring, Managed MLFlow, etc.
• Production-level experience with data ingestion, streaming technologies (i.e. PySpark, Spark, Kafka), performance tuning, troubleshooting and debugging
• Deep understanding of Machine Learning algorithms, techniques and methodologies with hands-on experience in applying supervised, unsupervised and deep learning techniques to real-world problems.
• Proficiency with programming languages such as Python, R, PySpark/Spark and/or Scala, including expertise with data manipulation and analysis libraries (i.e. NumPy, Pandas, etc.)
• Experience implementing real-time data processing pipelines for streaming analytics
• Experience with various DevOps functions and technologies (i.e. IaC with Terraform, CI/CD, automation tooling, etc.)
• Possesses strong problem-solving and analytical skills, with the ability to identify and resolve complex data engineering and data science challenges
• Possesses excellent communication and interpersonal skills, with the ability to collaborate effectively wi