Location: Kuala Lumpur, Malaysia
Title: Market Intelligence & Innovation Manager
About the Role
We are seeking a hands-on technical leader to design and build high-performance trading intelligence platforms. This role combines quantitative research with software engineering to develop ultra-low-latency, cloud-native trading systems. You will lead a team of quantitative developers, data scientists, and DevOps engineers to deliver AI/ML-powered trading solutions.
Key Responsibilities
- Platform Engineering: Design and build ultra-low-latency trading platforms with container orchestration (Kubernetes) and infrastructure-as-code (Terraform).
- Quantitative Research: Develop, backtest, and deploy algorithmic trading strategies and quantitative models.
- System Architecture: Architect event-driven microservices (Kafka) and low-latency APIs for real-time market data and trade execution.
- Cloud Operations: Deploy and optimize cloud-native platforms (AWS/Azure/GCP) with multi-region redundancy and auto-scaling.
- MLOps: Deploy AI/ML models for price prediction and signal generation using MLflow, Kubeflow, and feature stores.
- High-Performance Computing: Build distributed systems for Monte Carlo simulations, real-time risk analytics, and GPU-accelerated processing.
- Cybersecurity: Implement trading-critical security (zero-trust, secrets management, audit trails) and regulatory compliance (MiFID II, MAS).
- Team Leadership: Lead and mentor a team of quants, data scientists, and engineers while collaborating with traders and risk teams.
Required Qualifications
- 10-15 years in quantitative trading, algorithmic trading systems, or financial technology.
- 3-5 years in technical leadership managing quant or engineering teams.
- Expert programming in Python, C++, or Java with performance optimization focus.
- Deep expertise in distributed systems, microservices, and event-driven architectures (Kafka).
- Strong experience with Kubernetes, Terraform, and cloud platforms (AWS/Azure/GCP).
- Proven track record building and deploying production trading systems or quantitative strategies.
- Strong machine learning expertise (TensorFlow, PyTorch) and MLOps practices.
- Deep understanding of financial markets, market microstructure, and algorithmic trading.
Preferred Qualifications
- Master's or PhD in Computer Science, Financial Engineering, Mathematics, or related field.
- Cloud certifications (AWS Solutions Architect, Azure Solutions Architect, GCP Professional).
- Kubernetes certifications (CKA, CKAD, CKS).
- Experience with FIX protocol, HFT, or market making.
- CFA, FRM, or CQF certification.
