Crypto.com - Trading Analytics Developer, Quantitative Trading
Requirements
• Mandatory Foundations • 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 • Data Platform Expertise • 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 and BI tools • Kafka, Flink, and event processing in production environments • AI Platform Capabilities • Vector search system design and optimization (recall/latency/memory trade-offs) • 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 • LangGraph / LangChain to build AI workflow and to connect AI models with data and tools to create smarter applications. • Financial/Trading Domain • 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 • Professional Attributes • 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
Responsibilities
• Data Platform & Analytics • Design, build, and operate high throughput batch and streaming data pipelines using Kafka, Flink, and ETL technologies • 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 • AI Platform Development • Design, build, and operate internal AI platforms serving multiple trading teams • Develop vector search systems with optimized HNSW indexing and hybrid retrieval capabilities • Implement evaluation frameworks for retrieval quality (Recall@K, MRR, nDCG) and RAG systems • 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
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