Quick overview
Job description
Job Responsibilities
- Design, build, and operate high throughput batch and streaming data pipelines using Kafka, Flink, and ETL technologies
- Design and build unified analytics engine designed for processing large-scale data using Apache Spark and related tools
- Develop and optimize analytical data models for time-series, financial metrics, and trading activity
- Implement and manage analytical databases (ClickHouse, MongoDB, BigQuery, Snowflake, or similar) with cost-aware architecture
- Build idempotent data pipelines with robust backfill and reconciliation capabilities
- Create comprehensive monitoring for data quality, freshness, and pipeline reliability
- Design, build, and operate internal AI platforms serving multiple trading teams
- Build reusable AI tooling including standardized RAG pipelines, prompt management, and self-service workflows
- Create and maintain agent systems using modern frameworks (LangGraph, A2A, MCP) with focus on controllability and auditability
AI Platform Development
Job Requirements
- 5+ years production experience with both Python and Java in high-performance environments
- Strong software engineering fundamentals: system design, data structures, algorithms, data integrity, accuracy and performance optimization
- Expertise in Linux, Github, and modern CI/CD practices
- Proven experience with AWS cloud services and Kubernetes orchestration
- Comfort working with large-scale, complex datasets in financial/trading contexts
- Advanced SQL with window functions and query optimization, realtime data synchronization together with database design and infrastructure support
- Experience with data workflow and messaging orchestration (Airflow, Jenkins, AMPS etc.)
- Metric design and implementation for trading analytics (PnL, risk, balance and trade reconciliation, backfill and performance tuning)
- Time-series data visualization with Grafana, TradingView, web-based interactive dashboards and BI tools etc.
- Kafka, Flink, and event processing in production environments
- Retrieval system evaluation methodologies and quality frameworks
- RAG pipeline architecture and optimization techniques
- LLMOps practices including model lifecycle and prompt management
- Experience with AI agent frameworks in production settings like A2A and MCP
Data Platform Expertise
AI Platform Capabilities
Preferred Qualifications
- Experience in trading systems, quantitative finance, or financial technology
- Understanding of market data, data subscription using Rest API / Web Socket
- Knowledge of cryptocurrency markets, defi and related technologies
- Excellent problem-solving skills with ability to perform under pressure
- Strong communication skills for cross-team collaboration
- Proactive approach to system reliability and performance optimization
- Continuous learning mindset in rapidly evolving AI/ML landscape
- Balance of practical engineering rigor with innovative solution development
Professional Attributes
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