Machine Learning Engineer

Role Summary:

The Machine Learning Engineer on our Data Science team, plays a pivotal role in developing and deploying advanced machine learning models that drive our data-driven decision-making processes. The Machine Learning Engineer designs, implements and maintains MLOps and LLMOps pipelines for Greenbox Capital data science use cases. The MLE collaborates closely with data scientists, engineers, and business stakeholders to design, implement, and optimize scalable machine learning solutions.

Your expertise will be crucial in transforming raw data into actionable insights, enhancing our product offerings, and improving operational efficiency.

Key Responsibilities: 

• Designs and implements MLOps CI/CD pipelines for new data science initiatives
• Designs and implements LLMOps CI/CD pipelines for our cutting-edge Data & Analytics use cases
• Leverages Azure DevOps for tracking, managing, and prioritizing work efforts. 
• Partners with Product and development teams to achieve project objectives through iterative delivery.
• Collaborates with internal and external technology and business partners to implement changes and enhancements.

Education and Experience: 

• Masters Degree in Machine Learning, Computer Science, Quantitative Finance, Statistics, or Industrial Engineering.
• 5+ years of experience in a ML Engineering role.
• Specific experience with “scale and exit” organizations and their unique challenges to achieve large scale within smaller business constraints.
• Strong hands-on experience with ML models in various fields, e.g., Natural Language Processing, statistical learning theory, Large Language Models, computer vision.
• Experience with Agile methodologies, Azure DevOps (preferred), JIRA, or other work management tools.
• Ability to approach complex problems methodically and creatively.
• Strong team player who can work effectively with data scientists, engineers, and stakeholders.
• Comfortable with rapidly changing environments and technologies.
• Meticulous in ensuring data quality and pipeline reliability.
• Clear and concise in both written and verbal communication, able to explain technical concepts to non-technical stakeholders.
• Eagerness to stay updated with the latest trends and advancements in MLOps and data engineering.
• Proficiency in using Databricks for building and managing data pipelines.
• Experience with MLflow for tracking experiments, managing models, and deploying machine learning workflows.
• Knowledge of Delta Lake for ensuring data reliability and enabling ACID transactions.
• Strong skills in Python and SQL.
• Experience in building and optimizing data pipelines, ETL processes, and data integration.
• Understanding of machine learning algorithms and experience in model development and deployment using libraries such as TensorFlow, PyTorch, and Keras.
• Experience with continuous integration and continuous deployment (CI/CD) practices, using tools like Azure DevOps or GitHub Actions.
• Proficiency in Azure cloud services, including Azure Data Factory, Azure Storage, and Azure Machine Learning.
• Strong understanding of version control systems, particularly Git.
• Experience with data visualization tools like Power BI or Databricks SQL for reporting and analysis.

Preferred Qualifications: 

• Experience in the financial services or FinTech industry, particularly in MCA services.
• Certifications such as Databricks Certified Machine Learning Professional or Microsoft Certified Azure AI Engineer Associate

Behavioral/Skill Requirements:

• Demonstrated ability to analyze complex issues and use sound judgment to evaluate data, can easily draw conclusions, and make recommendations. Can easily explore alternative solutions, think creatively, and break down problems to understand their root causes.
• Demonstrated ability to learn from experience, can easily adapt to new situations, and apply lessons learned to achieve success. 
• Demonstrated ability to acknowledge mistakes and seek feedback for self-improvement. Is open-minded and explores new ways to approach challenges.
• Prior experience setting goals and delivering quality outcomes. Takes ownership and shows a sense of urgency in reaching objectives, measuring progress, and persistently working to overcome obstacles.
• Demonstrated ability to navigate uncertainty and change, while remaining productive even in unclear situations. Maintains focus and composure even when under pressure.  Can quickly shift priorities as needed and find ways to move forward without having all the answers.
• Demonstrated ability to prioritize and understand customer needs, both internal and external customers. Understands how one’s actions impact others and makes adjustments to align with team and organizational needs.
• Prior experience building and maintaining strong relationships with a wide range of people, demonstrating an understanding of social cues and adapting communication to different audiences. Balances assertiveness with diplomacy, fostering trust and cooperation.