Lead Software Engineer (Machine Learning)

As a Lead ML Engineer, you will play a leading role in operationalizing ML Models developed by bank’s data scientists. You will serve as the focal point for ML model refactoring, optimization, containerization, deployment, and quality monitoring.

Responsibilities

• Conduct reviews for compliance of the ML models in accordance with overall platform governance principles such as versioning, data / model lineage, code best practices and provide feedback to data scientists for potential improvements

• Develop pipelines for continuous operation, feedback and monitoring of ML models leveraging best practices from the CI/CD vertical within the MLOps domain

• Optimize AI development environments (development, testing, production) for usability, reliability and performance

• Have a strong relationship with the infrastructure and application development team in order to understand the best method of integrating the ML model into enterprise applications (e.g., transforming resulting models into APIs)

• Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines feeding these repositories and the ML feature or data stores are working as intended

• Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective

• Staying updated about the newest developments, patches and upgrades to the ML platforms in use by the data science teams

Requirements

• Minimum 10 years of hands-on experience as an ML Engineer, specializing in Python, ML libraries (e.g., Pandas, NumPy, TensorFlow, H2O), and large-scale data processing frameworks (e.g., Hadoop, Spark, Dask)

• Advanced proficiency in Python for both ML development and automation tasks

• Strong understanding of cloud platforms (AWS, GCP preferred) and container orchestration (OpenShift/Kubernetes)

• Extensive experience in building and maintaining CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions)

• Solid grasp of MLOps principles, including model operationalization, monitoring and scaling (experience with Kubeflow, Sagemaker, etc. is a plus)

• Proficiency in workflow orchestration (Airflow, Ctrl-M), logging & monitoring (Splunk, Geneos) as well as bash scripting and Unix/Linux command-line tools

• Experience in establishing and refining AI/ML development processes, standards and toolsets

We offer

• By choosing EPAM, you're getting a job at one of the most loved workplaces according to Newsweek 2021 & 2022&2023

• Employee ideas are the main driver of our business. We have a very supportive environment where your voice matters

• You will be challenged while working side-by-side with the best talent globally. We work with top-notch technologies, constantly seeking new industry trends and best practices

• We offer a transparent career path and an individual roadmap to engineer your future & accelerate your journey

• At EPAM, you can find vast opportunities for self-development: online courses and libraries, mentoring programs, partial grants of certification, and experience exchange with colleagues around the world. You will learn, contribute, and grow with us