The role involves developing trading analytics systems and integrating AI capabilities in a quantitative trading environment, utilizing Python, Java, and various data tools.
Eligible from: Unclear
Job Description
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.
Responsibilities
Job 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
Requirements
Job 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.
Preferred Qualifications
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
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