VP, Applied AI Engineer, AI Alpha Group
Summary
Build and deploy AI-driven investment systems end-to-end, integrating data pipelines, vector/graph databases, and full-stack apps to automate and enhance financial decision-making workflows.
- Implement and operationalize system components into production-ready applications.
- Translate product requirements into detailed technical specifications and development plans.
- Design, develop, and maintain full-stack applications using modern frameworks and best practices across backend and frontend systems.
- Design and implement evaluation frameworks, metrics and monitoring solutions to assess model and system performance; deploy applications to designated environments and ensure stable, reliable operation in production.
- Write and maintain unit, integration, and end-to-end tests to ensure code reliability, performance, and maintainability.
- Implement and manage data storage systems, including relational, vector, and graph databases, ensuring scalability, reliability, and integration with downstream applications.
- Design, build and optimize data processing workflows, including data extraction, transformation, integration pipelines, and feature engineering to support AI models and applications.
- Implement data quality checks, lineage tracking, and governance practices to ensure reliable, compliant, and auditable use of data across systems.
- Contribute to technical documentation and facilitate cross-functional collaboration throughout the product development lifecycle.
- Leverage AI-native development tools for coding, documentation, testing, and troubleshooting, while continuously exploring emerging AI techniques to enhance development productivity and quality.
- Bachelor's degree of higher in Computer Science, Software Engineering, or related field.
- 10+ years of experience designing, building and operating large-scale backend and data platforms, with hands-on expertise in Python-based backend frameworks (e.g., FastAPI, Django, Flask), and deep experience supporting AI- or analytics-intensive workloads.
- Strong proficiency in modern databases and frameworks (e.g., PostgreSQL, Spark, Kafka).
- Experience designing or working with vector databases or graph databases is preferred.
- Deep understanding of data modeling, feature engineering, and ELT pipeline design principles.
- Familiarity with data quality, metadata management, and governance frameworks.
- Experience with cloud and large-scale data platforms (e.g. Databricks, AWS or equivalents) is a plus.
- Working knowledge of modern machine learning workflows (feature engineering, model training, evaluation and iteration) and practical familiarity with LLMs, embeddings and prompt-based systems.
- Demonstrated ability to ship production-grade solutions and leverage AI-native development tools to accelerate coding, testing and documentation.
- Solid understanding of web architecture, APIs, and software design principles.
- Strong analytical, problem-solving, and communication skills, with the ability to collaborate effectively across technical and product teams.
- Deep curiosity and passion for exploring emerging AI technologies and applying them to real-world product development.
Our PRIME Values GIC is a values driven organization. GIC's PRIME Values act as our compass, enabling us to fulfil our fundamental purpose and objectives. It is the foundational bedrock which governs our behaviors, our decision making, and our focus. It informs both our long-term strategy as a firm, and the way we relate to our Client, business partners and employees. PRIME stands for Prudence, Respect, Integrity, Merit and Excellence.