Gravity IT Resources
Position Title: Senior ML/Ops Engineer
Position Type: 6-month contract
Location: Hybrid onsite in Miami, FL (4 days per week)
Work Authorization: U.S. Citizen or Green Card Holders only.
Referral Fee: $175 +/- per month based on hours worked.
The Machine Learning team at our client is responsible for architecting the productionized solution around rules-based and AI/ML models to integrate predictions seamlessly into the business processes, ensuring governance, resiliency, explainability, reproducibility, scalability of the models. We are looking for a high capable ML Platform Engineer to optimize rules-based and machine learning systems. As an engineer for the ML platform, you will be working at the intersection of Machine Learning, DevOps, and Data Engineering (i.e. MLOps).
Top Technical Requirements:
4) DevOps (CI/CD with GIT)
- Lead and consult with business stakeholders and data science teams to define data engineering and MLOps requirements.
- Transforming business and data science prototypes and applying appropriate algorithms and tools.
- Solving complex problems with multi-layered data sets, as well as, optimizing existing machine learning libraries and frameworks.
- Developing reusable data and feature stories for rules-based and AL/ ML models.
- Developing alerting tool framework for monitoring productionized model performance and effectiveness.
- Automate deployments incorporating MLOps best practices into productionized solutions.
- Document frameworks and machine-learning processes.
- 5+ years of overall experience in Data Analytics.
- 2+ years of experience with ML Engineering and/or MLOps.
- Bachelor’s degree in computer science, data science, mathematics, or a related field.
- Experience Agile Software Development.
- Experience in a large corporation or consulting firm.
- Experience with IoT and/or sensor data.
- Experience building scalable machine learning systems and data-driven products working with cross-functional teams.
- Well-developed software engineering fundamentals, including use of proper development, QA, and production environments, and the ability to write production-level code when needed.
- Experience creating python package.
- Proficiency in Python and experience with common data analytics packages (e.g. Numpy, Pandas, Sklearn, PySpark).
- Proficiency in Databricks and MLFlow.
- Proficiency in SQL.
- Good communication skills and the ability to understand & synthesize requirements across multiple project domains.
- Works effectively with cross-functional teams.