Fixed income relative value shop scaling AI infrastructure and commodity trading models
Garda Capital Partners deploys relative value strategies across fixed income markets for institutional investors, with a tech stack spanning Bloomberg, Murex, and FIX protocols alongside modern cloud infrastructure (AWS, Kubernetes, Azure). Active hiring across engineering, finance, and research—with 13 roles posted in the last 30 days—reflects aggressive expansion into AI tooling, commodity market modeling (diesel and oil products), and internal LLM integration. Pain points cluster around automating manual workflows and productionizing AI across the firm, suggesting engineering is moving faster than ops can absorb.
Garda Capital Partners is an institutional fixed income relative value manager founded in 2015 with 22+ years of combined strategy experience. The firm operates across seven primary offices: Wayzata (Minnesota HQ), New York, Scottsdale, Palm Beach, Geneva, Zug, Copenhagen, and Singapore. With 201–500 employees, Garda serves institutional investors exclusively and does not accept retail capital. Current development priorities include backend infrastructure for AI tools, a new commodity derivatives business (Atlantic Basin diesel expanding to all oil products and crude globally), APAC funding operations, and reporting/execution tools. The tech stack reflects a hybrid posture: legacy institutional finance tooling (Bloomberg, Murex, FIX, Excel, R) combined with modern engineering platforms (C#/.NET, Python, Kubernetes, Airflow, Dagster).
Bloomberg, Murex, FIX for trading infrastructure; C#/.NET, Python, React for application development; PostgreSQL, Oracle, SQL for data; AWS, Kubernetes, Azure for cloud; Airflow and Dagster for data orchestration; Excel and R for analytics.
Backend AI infrastructure, LLM API integration, a commodity derivatives business (diesel and oil products), APAC funding analytics, reporting/trading execution tools, and data platform modernization. Internal priorities include reducing manual workflows and productionizing AI across the firm.
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