Overview

We are seeking a Senior Machine Learning Engineer to join our core technical team, bringing expertise in building production-grade ML systems, data engineering and managing the full lifecycle of machine learning models. This role will contribute to multiple initiatives ensuring impactful solutions for real business challenges.

Responsibilities

  • Design and develop machine learning models for forecasting, classification, recommendation, segmentation and optimization
  • Package models for production and expose them via APIs or scheduled jobs
  • Implement monitoring, retraining and lifecycle management for ML solutions
  • Apply MLOps best practices, including model versioning, experiment tracking and reproducible pipelines
  • Monitor model performance in production and propose data-driven improvements
  • Participate in technical design reviews and propose well-reasoned solutions with trade-offs
  • Document architectural decisions and facilitate knowledge transfer to internal teams
  • Champion engineering standards, tools and best practices across the team
  • Collaborate with business stakeholders to translate problems into machine learning solutions

Requirements

  • 3+ years of experience in ML Engineering or Data Engineering working with production systems
  • Proven track record of delivering ML models actively used by real users with at least 2 live production projects
  • Proficiency in Python, PySpark and SQL
  • Skills in Scikit-learn, Databricks (production usage) and Delta Lake
  • Expertise with REST APIs, Git, CI/CD pipelines, Docker and Jenkins
  • Knowledge of MLflow for model versioning and experiment tracking
  • Background in time series forecasting, similarity techniques and computer vision models
  • Competency in feature engineering, model evaluation and monitoring
  • Strong communication skills to collaborate effectively with non-technical stakeholders
  • Ability to balance model simplicity and complexity with sound judgment
  • English proficiency at B2 level or higher

Nice to have

  • Experience in retail, fashion, consumer goods or distribution domains
  • Familiarity with enterprise planning tools like SAP IBP, SAP M3 or SAC
  • Background in building model monitoring dashboards with Power BI, Tableau or Looker
  • Knowledge of semantic similarity or embeddings in product catalogs
  • Understanding of multi-country or multi-currency platform challenges
  • Capability to design Lakehouse architectures such as Medallion or Data Mesh

[GTS] Benefits (generic, except India)

  • International projects with top brands
  • Work with global teams of highly skilled, diverse peers
  • Healthcare benefits
  • Employee financial programs
  • Paid time off and sick leave
  • Upskilling, reskilling and certification courses
  • Unlimited access to the LinkedIn Learning library and 22,000+ courses
  • Global career opportunities
  • Volunteer and community involvement opportunities
  • EPAM Employee Groups
  • Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn