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Lead Data ML Engineer (m/f/d) Germany

Lead Data ML Engineer (m/f/d) Description

Job #: 74248
Since 1993, EPAM Systems, Inc. (NYSE: EPAM) has leveraged its advanced software engineering heritage to become the foremost global digital transformation services provider – leading the industry in digital and physical product development and digital platform engineering services. Through its innovative strategy; integrated advisory, consulting, and design capabilities; and unique 'Engineering DNA,' EPAM's globally deployed hybrid teams help make the future real for clients and communities around the world by powering better enterprise, education and health platforms that connect people, optimize experiences, and improve people's lives. In 2021, EPAM was added to the S&P 500 and included among the list of Forbes Global 2000 companies.  

Selected by Newsweek as a 2021 and 2022 Most Loved Workplace, EPAM's global multi-disciplinary teams serve customers in more than 50 countries across six continents. As a recognized leader, EPAM is listed among the top 15 companies in Information Technology Services on the Fortune 1000 and ranked four times as the top IT services company on Fortune's 100 Fastest Growing Companies list. EPAM is also listed among Ad Age's top 25 World's Largest Agency Companies for three consecutive years, and Consulting Magazine named EPAM Continuum a top 20 Fastest Growing Firm.

Learn more at and follow EPAM on Twitter and LinkedIn.


Are you an expert in Machine Learning(ML) and do you have a deep understanding of Python ML as well as Apache Spark ecosystems? Do you have experience with MLOps concepts and related technology such as AWS SageMaker, Azure ML or GCP Vertex AI and others?
Have you been working with one of the most established Cloud providers for several years?

You will take responsibility for the design, development and lifecycle of ML pipelines based on best practices, as well as the optimisation of ML pipeline steps. You are in charge of Collaboration with Data Scientists and the Engineering Team to improve ML pipeline capacity and performance. You contribute to the research and understanding of new tools and techniques and drive improvement proposals when needed.


  • Own and contribute to ML pipeline design, development, and operating lifecycle based on best practices
  • Design, create, maintain, troubleshoot, and optimize ML pipeline steps
  • Own and contribute to the ML prediction endpoints design and Implementation
  • Deep configuration of cloud ML lifecycle management environment
  • Write specifications, documentation and user guides for developed applications
  • Productize prototype pipelines created by data scientists
  • If interested, cooperate with data scientists on the design and implementation of predictive models
  • Own collaboration with data scientists and engineering team to optimize ML pipeline capacity and performance
  • Support improvement of coding practices and repository organization in the data science work cycle
  • Contribute to the exploration and understanding of new tools and techniques and propose improvements
  • Establish and configure CI/CD, model registry
  • Define and advance MLOps best practices within data science and product teams
  • Continuously identify technical risks and gaps, devise mitigation strategies
  • Identify and cover hidden technical debt in ML systems


  • 3+ years experience as ML engineer or Data Engineer of designing, building and deploying production applications and data pipelines
  • Strong knowledge and experience in Python development
  • Deep understanding of Python ML ecosystem (numpy, pandas, sklearn, XGBoost, etc.)
  • Hands-on experience in implementation of Data Products
  • Understanding of Machine Learning fundamentals
  • Deep understanding of data preparation for ML
  • Understanding of Apache Spark Ecosystem (Spark SQL, MLlib/Spark ML)
  • Several years of hands-on experience in implementing or following SDLC best practices in complex IT projects
  • Experience with automated data pipeline and workflow management tools, i.e. Airflow
  • Deep knowledge and experience in computer science disciplines such as data structures, algorithms, and software design patterns
  • Hands-on experience in different data processing paradigms (batch, micro-batch, streaming)
  • Deep understanding of MLOps concepts and best practices
  • Experience with some of the MLOps related platform/technology such as AWS SageMaker, Azure ML, GCP Vertex AI / AI Platform, Databricks MLFlow, Kubeflow, Airflow, Argo Workflow, TensorFlow Extended (TFX), etc
  • Production experience in integrating ML models into complex data-driven systems
  • Experience with basic software engineering tools, e.g., git, CI/CD environment (such as Jenkins or Buildkite), PyPi, Docker, Kubernetes, unit testing, and general object-oriented design
  • Experience with one of the infrastructure as a code (IoC) frameworks (e.g.: Terraform / CDK TF, Ansible, AWS CloudFormation / AWS CDK, etc.)
  • Strong sense of ownership and growth mindset
  • Several years of experience working with one of the major Cloud

We offer

  • EPAM Employee Stock Purchase Plan (ESPP)
  • 30 days holiday per annum
  • Company Pension Scheme
  • Competitive compensation depending on experience and skills
  • Regular performance assessments
  • Opportunities for personal and professional growth
  • Unlimited access to LinkedIn learning solutions
  • Friendly and enjoyable working team
  • Relocation package support
  • Regular corporate and social events
  • Flexible and remote working opportunities
  • All benefits and perks are subject to specific eligibility requirements
  • Join EPAM Germany, named a Top Company 2022 by Kununu

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