Lead ML/Ops Engineer
Gravity IT Resources
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Position Title: Lead ML/ Ops Engineer
Position Type: 6-month contract
Location: Hybrid onsite in Miami, FL (3-4 days per week)
Work Authorization: U.S. Citizen or Green Card Holders only
Referral Fee: $175 +/- per month based on hours worked.
Position Overview:
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).
Responsibilities:
- 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.
Required Skills:
- 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.