Senior Quant Developer

8–12 years of experience in quantitative development or financial engineering, with a strong production track record in pricing and risk systems.

Expert-level Python — ability to design libraries from scratch, optimise hot paths (numpy vectorisation, Cython, multiprocessing), and set team-wide coding standards.

Deep fixed income pricing knowledge across multiple instrument types: rates derivatives (vanilla and exotic), credit products, structured products — including model calibration and Greeks.

Strong commodities derivatives knowledge — energy, metals, or agricultural markets; seasonality models, multi-factor commodity models.

Proven experience owning a quant model or library end-to-end in a production fintech or financial services environment.

Experience with CI/CD pipelines, automated testing frameworks, and production observability tooling.

Proven ability to lead and grow technical teams — mentoring junior/mid developers and contributing to hiring.

Degree (B.Tech / M.Tech / MSc / MFE) in a quantitative discipline — mathematics, physics, engineering, computer science, or financial engineering.

Experience with stochastic volatility models (SABR, Heston, LMM) and their numerical implementation, Monte-Carlo Simulation .

C++ expertise — writing or maintaining shared pricing libraries consumed by Python wrappers (pybind11, ctypes).

Familiarity with XVA (CVA, DVA, FVA) frameworks and their computational demands.

Experience with real-time risk (intraday VaR, P&L attribution) pipelines at scale.

Experience with distributed job orchestration (Airflow, Prefect) and large-scale EOD risk workflows.

Familiarity with real-time data ingestion frameworks (Kafka, Kinesis).

Experience with distributed computing frameworks (PySpark, Spark) for large-scale data processing.

Track record of delivering and operating robust quant systems at scale in a front-office or risk platform environment.

Experience designing systems for automated recovery, failover, and monitoring of analytics jobs.

Prior experience integrating complex model libraries into production platforms while maintaining reliability and transparency.

Demonstrated ability to act as a technical bridge between quant research, engineering, and client-facing teams.