Machine Learning Engineer

The Machine Learning Engineer will design, develop, and maintain the productionization of machine learning, deep learning, generative AI, large language models, simulation, and optimization algorithms. This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work will be done in Databricks and PySpark.

The ideal candidate will have excellent technical proficiency, excellent communication skills, a self-driven mindset, and the willingness to continuously learn new things.

This position will report to the Director of Business Intelligence and is structured within IT under the Vice President of Applications.

The position will be located in Arlington, VA and will require commuting to the office 5 days a week.

Responsibilities

• Work with business stakeholders to define project requirements.
• Orchestrate, scale, setup and improve model serving pipelines.
• Improve model accuracy through feature engineering, tuning, and observability.
• Improve model computational performance through all aspects of the pipeline, including tuning clusters/job compute, partitioning, caching, feature engineering code, tuning setup, etc.
• Integrate machine learning models into production environments, ensuring reliability and scalability.
• Evaluate pretrained models and software from vendors and support integration into production environments.
• Develop comprehensive project plans for implementing machine learning and AI projects including solution architectures, resourcing, and dependencies.
• Provide ETL requirements to data engineers to effectively curate files for data analytics.
• Work with data scientists, data engineers, and business analysts to translate business requirements into machine learning solutions.
• Build software solutions that are maintainable, scalable and provide quantifiable business value.
• Continuously focus on quality architecture, quality code, and ruthless management of technical debt.
• Continuously push the practice forward, learning and testing newer and better ways of performing work.

• 5 years of machine learning engineering, software engineering, or data science experience.
• Bachelors in a quantitative field of study.

Preferred Qualifications

• Masters in a quantitative field of study.
• Experience with the Azure, AWS, or other cloud ecosystems.
• Experience in building secure data processing pipelines.
• Proficient in utilizing data lakes, CI/CD pipelines, Databricks, Unity Catalog, and Git.
• Experience working with streaming.
• Expertise in building machine learning solutions using cloud data services.
• Exceptional skills in data processing languages such as SQL, Python, or Scala.
• Exceptional skills in feature engineering, model optimization, and parameter tuning.

Venture Global LNG ("Venture Global") is a long-term, low-cost provider of American-produced liquefied natural gas. The company's two Louisiana-based export projects service the global demand for North American natural gas and support the long-term development of clean and reliable North American energy supplies. Using reliable, proven technology in an innovative plant design configuration, Venture Global's modular, mid-scale plant design will replace traditional designs as it allows for the same efficiency and operational reliability at significantly lower capital cost.