Lead Machine Learning Engineer

JOB OVERVIEW

The Lead Machine Learning Engineer is a senior technical leader who independently designs, builds, and owns complex, end-to-end machine learning systems that drive significant business impact. A key aspect of this role is the ability to quickly iterate on experiments and translate them into tangible business value. You will guide the technical direction for major projects, mentor other engineers, and begin to influence technical strategy beyond your immediate team. You are expected to tackle ambiguous, open-ended problems, defining not only the technical solution but also the path to successful execution and delivery of customer value.

RESPONSIBILITIES & DUTIES

• Lead the design, implementation, and deployment of machine learning systems. Take ownership of team-level goals by defining technical roadmaps for complex projects, often involving cross-team dependencies.
• Partner with product managers and stakeholders to translate business needs into well-defined machine learning problems. Play a key role in setting the team's medium-to-long-term technical strategy and proposing new business initiatives powered by ML.
• Act as a role model and mentor for other engineers, leveling up the team's craft through code/design reviews and establishing best practices. Drive collaboration with cross-functional partners to deliver robust, integrated solutions.
• Ensure the quality, scalability, and maintainability of the team's ML systems. Define and validate experimentation strategies. Stay current with state-of-the-art ML developments and incorporate new techniques and tools to drive innovation and business value.
• Design robust and scalable ML systems and components that interface effectively with adjacent infrastructure and product systems. Make sound technical decisions that balance immediate needs with long-term extensibility and maintainability.
• Evaluate and prototype generative AI solutions where they align with business needs, assessing their feasibility, potential impact, and ethical implications.
• Deliver machine learning solutions that measurably improve key business metrics, such as client sentiment, revenue generation, or cost reduction.
• Establish a track record of efficiently iterating on experiments, with a demonstrated ability to move projects from the prototyping phase to production deployment within agreed-upon timelines.
• Significantly contribute to the technical development of team members through active mentorship and guidance, as evidenced by improved code quality, adoption of best practices, and positive feedback.
• Propose and drive forward multiple new initiatives leveraging machine learning that align with the company's strategic goals and demonstrates innovative problem-solving.

QUALIFICATIONS

• Bachelor's Degree in Computer Science, Data Analytics, Mathematics or related field preferred
• Minimum 5 years of professional machine learning experience
• Banking and financial services experience a plus

PROFESSIONAL CERTIFICATIONS

• None required

TECHNICAL SKILLS

• To be successful in this role, you should have experience with and an understanding of the following:
• Expert ability to synthesize insights from large, complex datasets and translate them into actionable strategies and product recommendations.
• Mastery of Python for machine learning development, including expertise in libraries such as scikit-learn, TensorFlow, PyTorch, and Transformers. Proficiency in other languages (e.g., Go, Java, Scala) for building scalable and performant systems is a plus.
• Advanced proficiency in SQL and experience with various database systems (e.g., PostgreSQL, MS SQL, Presto). Ability to design and optimize data pipelines for efficient data retrieval and processing.
• Deep understanding of cloud service providers, particularly AWS, and their ML-related services (e.g., SageMaker, Bedrock, EC2, ECS). Experience designing and deploying ML solutions in a cloud environment.
• Expertise in using IaC tools (e.g., AWS CDK) to automate the deployment and management of ML infrastructure. Ability to define and manage reproducible ML environments.
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