Quantitative Developer

We are seeking a highly skilled AI Engineer with strong expertise in machine learning operations (ML Ops), cloud engineering, and large-scale data systems to support enterprise AI initiatives. This role is engineering-focused, emphasizing infrastructure, automation, and integration rather than model development. The ideal candidate has experience with end-to-end ML model lifecycle management, scalable system architecture, and enterprise AI applications in a cloud environment.

Key Responsibilities:

• ML Ops &

Infrastructure: Design, implement, and maintain scalable, cloud-based ML pipelines using AWS (Sage Maker, Unified Studio, Mayflower or equivalents) for model deployment, monitoring, and automation.

• Big Data & Cloud Engineering:
Build and optimize high-performance data pipelines and distributed computing solutions for processing large-scale datasets.

• Enterprise AI Systems:
Develop and integrate AI-driven solutions into enterprise-grade financial applications (CCFA apps), ensuring compliance with common standards, security, and best practices.

• Software Development:
Write production-ready code in Python, C++, and other relevant languages to support AI system implementation and infrastructure scaling.

• Database & Performance Optimization:
Work with SQL, No

SQL, and large-scale database systems to ensure efficient data retrieval, transformation, and storage for AI applications.

• Collaboration & Architecture:
Partner with data engineers, cloud architects, and quant engineers to develop and maintain robust AI-driven workflows in a cloud-based, enterprise environment.

• Model Lineage & Governance:
Implement ML model lifecycle tracking, data lineage, and governance frameworks to ensure AI system transparency and compliance.

Qualifications:

• 5+ years of experience in software engineering, cloud infrastructure, or ML Ops.

• Strong programming skills in Python, C++, SQL, and experience with cloud-based AI services (AWS Sage Maker, Mayflower, Unified Studio, etc.).

• Deep understanding of ML Ops, model lifecycle management, and AI deployment strategies.

• Experience working with large-scale, enterprise applications, preferably in financial services.

• Familiarity with big data processing frameworks (Spark, Kafka, or similar) and cloud-based AI/ML pipelines.

• Strong problem-solving skills and ability to work with cross-functional teams in a fast-paced environment.

This role is ideal for an experienced engineer who understands AI/ML workflows but focuses on infrastructure, deployment, and scaling rather than developing new ML models. If you are passionate about AI-driven engineering, cloud automation, and enterprise-grade AI solutions, we encourage you to apply.

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