Senior Geospatial ML Engineer building satellite - imagery vegetation intelligence for a climate - tech company. Improving production models and pipelines to help utilities prevent wildfires and outages.
About the role
- Lead AI/ML Engineer designing and optimizing algorithms at Xsolla, enhancing gaming solutions for developers globally.
Responsibilities
- Design, build, and optimize algorithm in Vertex ai.
- Develop scalable data models, Algorithm 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, 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.
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**.
- **Preferred**
- 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
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