Lead AI/ML Engineer

ABOUT YOU   We're seeking a Lead AI/ML Engineer to architect and scale our data infrastructure supporting personalization, churn prediction, and recommendation systems across Xsolla's global gaming commerce platform. You'll lead the design and optimization of ML algorithms in Vertex AI, build scalable data pipelines integrating multiple sources, and establish best practices for data quality, governance, and performance. This role combines hands-on technical leadership with cross-functional collaboration, mentoring engineers, partnering with data science and backend teams to productionize ML features, and working directly with the Director of Data Platforms to translate business needs into technical solutions. You'll be instrumental in shaping our data mesh architecture while ensuring compliance, scalability, and operational excellence as we serve game developers worldwide.   ABOUT US   Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com.

Responsibilities:

Design, build, and optimize algorithms in Vertex AI

Develop scalable data models and algorithms supporting user 360 views, churn prediction, and recommendation engine inputs

Lead integration across data sources: MySQL, BigQuery, Redis, Kafka, GCP Storage, and API Gateway

Implement CI/CD for data pipelines using Git, dbt, and automated testing

Define data quality checks and auditing pipelines for ingestion and transformation layers

Mentor and guide junior AI/ML engineers on data modeling and algorithm performance tuning

Partner with Data Science, ML, and Backend teams to productionize machine learning features in Snowflake

Work closely with Legal, Security, and Infrastructure teams to ensure compliance, privacy, and governance of user data (PII)

Collaborate with the Director of Data Platforms and product stakeholders to translate business requirements into technical specifications

Tune algorithm performance

Establish data partitioning, clustering, and materialized views for fast query execution

Build dashboards and monitors for pipeline health, job success, and data latency metrics (e.g., via Looker, Tableau, or Snowsight)

Establish and enforce naming conventions, data lineage, and metadata standards across schemas

Lead code reviews, enforce documentation standards, and manage schema versioning

Contribute to the company's evolving data mesh and streaming architecture vision

Qualification & Skills:

5+ years of experience in AI/ML engineering, with 3+ years in Vertex.ai.

Strong SQL and Python skills, with proven experience building ETL/ELT at scale.

Deep understanding of algorithm performance tuning, query optimization, and warehouse orchestration.

Experience with data pipeline orchestration (Airflow, Prefect, dbt, or similar).

Solid understanding of data modeling (Kimball, Data Vault, or hybrid).

Proficiency in Kafka, GCP, or AWS for real-time or batch ingestion.

Familiarity with API-based data integration and microservice architectures.

Nice to Have:

Experience lead machine learning teams or/and deploying ML feature pipelines.

Background in ad-tech, gaming, or e-commerce recommendation systems.

Familiarity with data contracts and feature stores (Feast, Tecton, or custom-built).

Experience managing small data engineering teams and setting technical direction

Strong ownership and ability to work autonomously in a fast-paced environment.

Excellent cross-functional communication — can translate between engineering and business.

Hands-on problem solver who balances velocity with reliability.

Collaborative mentor who raises the bar for team quality and discipline