Senior MLOps Engineer

EPAM Systems; Chile; On-site; CLP 42,000,000 - 60,000,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off & sick leave

Upskilling & certification courses

Global career opportunities

Volunteer & community involvement

Job summary

EPAM Systems is seeking a Senior MLOps Engineer to join an MVP engagement for a high-profile game publisher, building a test intelligence platform for two game franchises. The role emphasizes full re-derivability, model lineage, and reproducibility from day one as the system evolves with franchise history.

You will own signal catalog operations, manage semantic vector index versioning, and design back-test/harness workflows while enforcing rigorous calibration and holdout discipline to drive

Qualifications

• 3+ years in MLOps or ML platform engineering.
• Expertise in ML model lifecycle management including versioning/configuration/rollback.
• Background in signal computation pipeline design, coverage of refresh/provenance.
• Proficiency in calibration methodology: holdout discipline, sweep design, catch-rate vs. scope.
• Knowledge of as-of temporal data systems or back-test harnesses.
• Skills in Python and SQL for ML pipeline automation.
• Competency in model monitoring including drift detection and run-behavior drift counters.
• Ability to collaborate with data engineers and AI developers on feature alignment.
• Document calibration procedures, signal definitions, and operational runbooks.

Responsibilities

• Own Signal Catalogue operations and provenance capture across signals.
• Coordinate with AI developers on model/dimension stamp conventions and reindex paths.
• Design, implement, and own the Back-test & Calibration Harness.
• Enforce holdout patch-set discipline and publish winning configurations as weight proposals.
• Lead Model Generation & Experimentation with systematic experimentation framework.
• Maintain model lineage across configuration versions for both franchises.
• Implement MLOps Monitor and Data-Health Monitor across all ingestion channels.
• Support exploration cadence with unbiased random-sample injections.
• Contribute to operator runbooks covering signal refresh, calibration campaigns, and embedding reindex procedures.

Skills

MLOps

ML lifecycle

Python

SQL

Model monitoring

Collaboration

Documentation

Spec-Driven Dev

English B2

Tools

MLflow

Kubeflow

Snowflake ML

Snowpark

pgvector

Job description

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
• Knowledge of as-of temporal data systems or back-test harness design and operation
• Skills in Python and SQL for ML pipeline automation
• Competency in model monitoring, including drift detection, catch-rate floor monitoring, and run-behavior drift counters
• Capability to collaborate with data engineers and AI developers on feature alignment
• Qualifications in documenting calibration procedures, signal definitions, and operational runbooks
• Familiarity with Spec Driven Development
• English proficiency at an Upper-Intermediate level (B2) or higher

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.