Trading Analytics Developer responsible for advancing data and AI infrastructure in crypto trading. Bridging quantitative development and AI platform engineering to drive insights and performance.
Responsibilities
The Quant Trading team is responsible for trading and managing risks associated with different crypto products, including spots and derivatives.
The team develops and implements trading strategies in fast-paced and complex trading environments.
We are seeking an experienced Trading Analytics Developer to join our Quant Trading team and play a pivotal role in advancing our data and AI infrastructure.
This role combines traditional quantitative development with cutting-edge AI platform engineering, focusing on building robust, scalable systems that serve both data analytics and artificial intelligence workloads.
The ideal candidate will bridge the gap between high-performance trading systems and modern AI capabilities, ensuring reliability, performance, and actionable insights across both domains.
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 and BI tools
Kafka, Flink, and event processing in production environments
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.
Benefits
Competitive salary
Medical insurance package with extended coverage to dependents
Attractive annual leave entitlement including: birthday, work anniversary
Work Flexibility Adoption. Flexi-work hour and hybrid or remote set-up
Aspire career alternatives through us. Our internal mobility program can offer employees a diverse scope.
Work Perks: crypto.com visa card provided upon joining
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