Job Description
We build AI-powered products internally and support portfolio companies with complex data and AI infrastructure challenges. We are looking for an engineer who enjoys building the foundation beneath AI systems: data pipelines, backend services, storage architecture, APIs and the infrastructure that turns messy real-world data into usable products.
This is a hands-on role with significant ownership and direct exposure to founders, operators, and investors.
The role
You will own the data and backend layer behind AI products and internal platforms.
This is not a pure ML role. We need someone who enjoys designing databases, building ingestion systems, operating data infrastructure, and shipping production services. AI sits on top of that foundation.
Stack
Python • PostgreSQL • ClickHouse • Airflow / Dagster • dbt • AWS / GCP • Docker • Kubernetes • OpenAI • Anthropic • Vector Databases
What you'll do
- Design and build data lakes, warehouses, and backend systems
- Build data ingestion services, APIs, ETL/ELT pipelines, and workflow orchestration
- Design relational and analytical databases (Postgres, ClickHouse, BigQuery, DuckDB or similar)
- Build infrastructure for LLM applications, RAG systems, embeddings, and retrieval pipelines
- Deploy and operate production systems using Docker, Kubernetes, CI/CD, and cloud infrastructure
- Work directly with founders and product teams on data-intensive and AI-driven products
What we expect
- 5+ years building production backend systems
- Strong Python
- Strong database design and data modelling experience
- Experience building and operating data pipelines at scale
- Experience with modern data tooling (Airflow, Dagster, dbt, Kafka, Spark, etc.)
- Practical experience with LLM systems, RAG, vector databases, or AI infrastructure
- Cloud infrastructure experience (AWS, GCP, or Azure)
- Russian fluent or native (required)
Nice to have
Go • TypeScript • ClickHouse • BigQuery • DuckDB • Kafka • Spark • ML frameworks • Forecasting • Recommendation Systems • Time-Series Data • Geospatial Data • Startup Experience
What we offer
- Fully remote
- Direct access to founders and investors
- Work across multiple AI products and companies
- High ownership and autonomy
- Exposure to both engineering and venture investing
- Competitive compensation, discussed individually