Quantitative Developer, Risk Technology

Jain Global•New York, NY

About The Position

Jain Global is seeking a software engineer to join its Risk Technology team. This engineering-first role owns platforms that capture positions, calculate and distribute risk, and deliver exposure and limit information to risk managers and portfolio managers in near real time. The work spans low-latency services, large-scale data pipelines, distributed computing, and the APIs and interfaces built on top of them. The developer will work with quantitative risk analysts who own the models, ensuring those models run correctly, quickly, and reliably at firm scale across the firm's asset classes. The role includes engineering risk calculation and exposure aggregation services; developing real-time and intraday monitoring, limits, breach detection, alerts, and dashboards; building data pipelines and analytical stores; integrating with external financial and market-data platforms; and productionizing risk models. It also owns system reliability, performance, operations, and engineering standards, while collaborating with risk, investment, trading, IT, data, and platform teams.

Requirements

BS or MS in Computer Science, Engineering, Mathematics, Physics, or a related quantitative field.
At least 5 years building and operating production systems.
Deep expertise in Python and at least one systems language: C++, C#, Java, or Rust.
Strong grounding in distributed systems, concurrency, service design, and API design, with experience owning production systems.
Engineering discipline in automated testing, code review, CI/CD, observability, and infrastructure as code.
Comfort with performance work, including profiling, benchmarking, and reasoning about latency and throughput.
Proficiency with SQL and analytical or columnar stores, plus time-series technologies.
Experience with streaming and messaging systems and workflow orchestration.
Hands-on experience with containers, Kubernetes, at least one major cloud platform, and grid or distributed compute frameworks.
Track record handling large-scale data volumes where correctness and timeliness both matter.
Pragmatic problem-solving ability, high standards for correctness, and attention to detail under time pressure.
Clear communication skills and ability to work directly with risk managers and traders, translating business needs into technical designs.
Self-motivation, proactivity, and comfort owning systems in a demanding, fast-moving environment.

Nice To Haves

An advanced degree.
Experience at a hedge fund, asset manager, investment bank, or similar institution, ideally supporting risk, valuation, or front-office systems.
Working familiarity with multi-asset instruments and derivatives, and practical risk measurement and monitoring, including VaR, stress testing, sensitivities, limits, margin, and financing.
Experience with time-series technologies such as kdb+/q, ClickHouse, or Arctic, or similar.
Experience with messaging tools such as Kafka or Redis, or equivalent, and workflow orchestrators such as Airflow, Dagster, or in-house schedulers.

Responsibilities

Design, build, and own risk calculation and exposure aggregation services, from position capture through delivery of risk results.
Develop real-time and intraday risk monitoring systems, including limit frameworks, breach detection, alerting, and drill-down interfaces.
Build documented REST, gRPC, and streaming APIs and services that expose risk data to downstream firm consumers.
Deliver front-end tools and dashboards for slicing exposure by strategy, desk, asset class, factor, and counterparty.
Modernize legacy risk processes by replacing batch, spreadsheet, and manual steps with tested, version-controlled services.
Build resilient pipelines for positions, trades, market and reference data, and counterparty exposures, with validation, lineage, and reconciliation.
Own time-series and analytical data stores for historical risk, stress replays, and time-travel queries.
Integrate with prime brokers, clearing venues, execution platforms, and market-data vendors, handling schema changes and outages.
Ensure pricing, position, and P&L data are consistent between risk systems and Front Office and Finance platforms.
Productionize risk models supplied by Risk Management and Research, including VaR, stress and scenario frameworks, factor exposures, sensitivities, and margin analytics.
Translate research prototypes into performant, tested, maintainable production code with numerical validation.
Build tools for model owners to backtest, recalibrate, and compare model versions without engineering involvement.
Maintain pricing and sensitivity (Greeks) infrastructure and libraries used by risk and valuation.
Own end-to-end reliability of risk systems, including monitoring, alerting, runbooks, on-call, and incident follow-up.
Profile and optimize performance-sensitive paths, including vectorization, caching, concurrency, memory layout, and distributed or grid compute workloads.
Meet overnight and intraday risk production deadlines with automated recovery and clear failure semantics.
Build CI/CD, automated testing, infrastructure as code, and release processes for systems that must not silently produce incorrect numbers.
Partner with Market Risk, Credit Risk, the CRO's office, and Portfolio Managers to translate requirements into shipped software.
Work with Front Office quantitative and trading technology teams on shared pricing, position, and market-data infrastructure.
Collaborate with enterprise IT, data, and platform teams on cloud, networking, security, and compute capacity.
Support regulatory reviews, investor due diligence, and internal risk governance with reliable data and clear technical documentation.
Set engineering standards for the risk stack, including code review, testing, documentation, and architectural direction.
Mentor junior developers, improve delivery quality across the team, and evaluate and lead adoption of useful technologies.