Lead AI/ML Engineer

Skills

About the Role

You will architect and scale the data infrastructure supporting personalization, churn prediction, and recommendation systems across a 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. You'll combine 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 help shape the data mesh architecture while ensuring compliance, scalability, and operational excellence as the company serves game developers worldwide.

Requirements

• 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

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 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

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• PTO