Senior ML Engineer

Accountabilities • Lead ML platform engineering initiatives: Design, develop, deploy, and operate complex platform capabilities that support the complete machine learning lifecycle, ensuring systems are reliable, scalable, and maintainable.

• Build scalable ML infrastructure: Develop robust solutions for model deployment and serving, feature computation, data validation, workflow orchestration, monitoring, and production support.

• Improve platform reliability and performance: Build reusable components, standards, and automation that strengthen system availability, scalability, observability, operational readiness, and cost efficiency.

• Own technical projects end to end: Translate business requirements into technical designs, evaluate implementation options, communicate trade-offs, coordinate delivery, measure outcomes, and ensure successful production launches.

• Drive production troubleshooting: Lead investigations into complex production incidents, identify root causes, implement durable solutions, and establish preventive measures to reduce recurring issues.

• Collaborate across technical and business teams: Partner with Data Science, Product, Risk, Fraud, and Engineering stakeholders to understand business needs and deliver effective platform solutions.

• Shape technical strategy and architecture: Contribute to platform architecture, establish engineering standards, evaluate emerging technologies, and influence long-term technical direction.

• Strengthen engineering quality: Conduct thoughtful code and design reviews, improve testing and development practices, and promote maintainable software design across the engineering organization.

• Mentor and support engineers: Share technical knowledge through mentoring, pairing, and constructive feedback, helping colleagues develop their skills and deliver high-quality solutions.

• Improve the ML lifecycle: Support reliable processes spanning feature engineering, model evaluation, deployment, serving, monitoring, and governance, ensuring machine learning systems operate effectively in production.

• Leverage modern engineering tools: Apply AI-assisted development tools and automation where appropriate to reduce repetitive work, accelerate problem-solving, and improve engineering productivity.

• Connect engineering to business outcomes: Ensure platform investments deliver measurable improvements in operational efficiency, developer experience, system reliability, and customer outcomes.

Requirements

• Professional software engineering experience: At least 5 years of experience building, deploying, and operating production software systems, with strong programming skills in Python.

• Strong software engineering fundamentals: Deep understanding of API design, distributed systems, service architecture, software design principles, testing strategies, and maintainable code development.

• Cloud-native and scalable systems: Demonstrated experience designing and operating reliable, scalable cloud-native services or data-intensive systems in production environments.

• ML platform and MLOps expertise: Practical experience with machine learning platforms, model serving, feature engineering, data platforms, MLOps, or closely related infrastructure systems.

• Understanding of the ML lifecycle: Familiarity with the processes involved in feature engineering, model training, evaluation, deployment, inference, monitoring, and governance.

• Infrastructure and deployment technologies: Experience with Docker, Kubernetes, CI/CD pipelines, Git-based workflows, and production observability tools.

• Data and database proficiency: Practical experience with SQL, databases, caching systems, and tracing data or requests across distributed services.

• Production ownership: Ability to investigate complex technical problems, make sound architectural decisions, manage risks and dependencies, and deliver reliable solutions with minimal implementation guidance.

• Technical leadership: Demonstrated ability to lead significant engineering initiatives, mentor colleagues, provide constructive feedback, and influence technical decisions across teams.

• Communication and collaboration: Strong communication skills, with the ability to explain technical trade-offs clearly, align stakeholders, and connect engineering priorities to business, product, and operational objectives.

• Adaptability and independent judgment: Comfort working with ambiguity, navigating incomplete information, evaluating alternatives, and taking ownership of decisions and outcomes.

• AI-assisted engineering mindset: Willingness to experiment with AI tools, identify practical applications, and combine automation with sound engineering judgment to improve productivity.

Additional qualifications that are advantageous:

• Experience with cloud and data technologies such as AWS, Databricks, MLflow, Amazon SageMaker, Spark/PySpark, Kafka, or other streaming systems.

• Experience developing feature platforms, model-serving infrastructure, workflow orchestration systems, or internal developer platforms.

• Familiarity with monitoring model quality, data quality, feature coverage, inference latency, system reliability, and infrastructure costs.

• Experience working with sensitive data in regulated industries, particularly financial services.

• Proficiency in Java or another backend programming language in addition to Python.

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