Senior MLOps Engineer

EPAM Systems; Colombia; On-site; COP 120,000,000 - 210,000,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off

Upskilling and certifications

Global career opportunities

Employee resource groups

Job summary

EPAM Systems is seeking a Senior MLOps Engineer to join an MVP engagement for a major AAA game publisher. You will build a test intelligence platform for two game franchises with a focus on replayable configurations, versioned signals, and calibration-driven weight updates in the Settings View.

You will lead productionization of ML components, ensure reproducibility, and drive long-term value as models and data mature.

Qualifications

• 3+ years in MLOps or ML platform engineering.
• Expertise in ML model lifecycle management, including versioning and rollback.
• Background in signal computation pipeline design and provenance capture.

Responsibilities

• Own Signal Catalogue operations, including refresh orchestration and provenance capture across signals.
• Coordinate model/dimension stamp conventions with AI team; manage reindex path when gateway model changes.
• Design and maintain the Back-test & Calibration Harness with temporal filtering and replay runner.
• Enforce holdout patch-set discipline and configuration sweeps with tracking of catch-rate vs. scope.
• Lead Model Generation & Experimentation across score configurations and publication of winning proposals.
• Maintain model lineage across configuration versions for both franchises.
• Implement MLOps Monitor and Data-Health Monitor for run drift and ingestion telemetry.
• Support exploration cadence with unbiased random-sample injections and provenance.
• Contribute to operator runbooks covering signal refresh, calibration campaigns, and embedding reindex procedures.

Skills

MLOps

ML lifecycle

Signal pipeline

Calibration

Experimentation

Tools

MLflow

Kubeflow

Snowflake ML

Snowpark

pgvector

Job description

EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

We are seeking a Senior MLOps Engineer to join an MVP engagement with a major AAA game publisher, building a test intelligence platform for two game franchises in parallel. A core design principle is full re-derivability and model lineage from day one — every run must be replayable from its stored configuration version and feed read positions. The signal catalog feeds a scoring strategy engine with versioned configurations, and calibration sweeps over historical data produce suggested weight updates surfaced directly in the Settings View.

This role ensures the ML and signal components are production-ready, reproducible, and improvable over time, forming the foundation of the system's long-term value as franchise history accumulates and models are refined.

Responsibilities

• Own Signal Catalogue operations: signal refresh orchestration triggered by feed read-position advances, grain translation between per-test, per-area, and per-run signal families, and provenance capture across all 8 signals
• Operate the Semantic Vector Index versioning: coordinate with the Senior AI Developer on model and dimension stamp conventions; design and execute the controlled reindex path when the enterprise AI gateway model changes
• Design, implement, and own the Back-test & Calibration Harness: as‑of temporal filtering across all record families, replay runner, look‑ahead spot audit, and configuration sweep runner
• Enforce holdout patch‑set discipline, configuration sweep over route limits, thresholds, weights, and Composition setting; produce catch‑rate vs. scope tables per candidate configuration and publish winning configurations as suggested‑weight proposals into the Settings View
• Lead Model Generation & Experimentation: systematic experimentation framework over scoring strategy configurations, tracking which signal weights and route combinations yield the best catch‑rate vs. scope trade‑off
• Maintain model lineage across configuration versions for both franchises
• Implement the MLOps Monitor and Data‑Health Monitor: catch‑rate floor monitoring, run‑behaviour drift counters (per‑run candidate volumes per route, score distribution vs. usual range), data‑health telemetry across all ingestion channels
• Manage exploration cadence support: unbiased random‑sample injection with provenance ensuring exploration entries are never counted as model recommendations
• Contribute to operator runbook sections covering signal refresh, calibration campaigns, model generation runs, and embedding reindex procedures

Requirements

• 3+ years of experience in MLOps or ML platform engineering
• Expertise in ML model lifecycle management, including versioning, configuration management, and rollback
• Background in signal computation pipeline design, covering scheduled refresh, provenance capture, and grain translation
• Proficiency in calibration methodology: holdout discipline, configuration sweep design, and catch‑rate vs. scope measurement

  Nice to have

  • Understanding of MLflow, Kubeflow, or equivalent experiment tracking platforms
  • Familiarity with Snowflake ML or Snowpark
  • Showcase of embedding model versioning and controlled reindex orchestration
  • Skills in pgvector or vector store operational management
  • Background in gaming domain or QA tooling

  We offer

  • 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

  EPAM is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, disability, protected veteran status, or any other characteristic protected by applicable law.