Senior Functional Developer

Crisil; Mumbai; On-site; INR 1,200,000 - 1,700,000; Full time

Job summary

Crisil is seeking a Quant Developer / Software Engineer to work with model developers and Front Office quants, translating complex quantitative concepts into reliable Java-based services. You will contribute to the design, development and support of quantitative models, and build production-quality microservices designed for cloud-native environments.

Strong emphasis on modern Java, Spring Boot, Python tooling, automated tests and robust software engineering practices to ensure accuracy and

Qualifications

• Strong software engineering experience with Java and Spring Boot, including production-quality backend services.
• Strong hands-on experience with Python for implementing, analysing and validating quantitative models and prototypes.
• Strong mathematical and analytical background, with ability to translate formulas into working software.
• Ability to take a model from quantitative specification and Python prototype through to robust production Java implementation.
• Good understanding of algorithms, data structures, and software engineering principles.
• Experience with automated unit, integration and regression tests, validating numerical results.
• Experience with relational databases and SQL.
• Familiarity with Git, Maven, Azure DevOps and Confluence.

Skills

Java

Spring Boot

Python

Mathematical background

Analytical thinking

English communication

Software engineering

Tools

Git

Maven

Azure DevOps

Confluence

Docker

Kubernetes/OpenShift

Linux

Job description

• Work closely with model developers and Front Office quants to understand new proxy model requirements and translate quantitative concepts into implementable solutions.
• Contribute to the design, development and support of quantitative models, particularly models used to derive or proxy market data.
• Create clear technical and quantitative documentation describing model assumptions, inputs, calculations, limitations and implementation decisions.
• Develop prototype implementations to demonstrate and validate new models before production implementation.
• Translate validated prototypes and quantitative algorithms into production-quality Java services.
• Build scalable, resilient microservices-based applications designed to run in a cloud-native environment.
• Influence technology choices and contribute to the architecture and design of new components and services.
• Develop applications using modern Java and Spring Boot, following strong software-engineering practices.
• Design and maintain automated unit, integration and regression tests, including tests that validate quantitative results.
• Work with engineers and quantitative stakeholders to ensure implementations remain consistent with their underlying model specifications.
• Contribute to CI/CD and operational readiness, using technologies such as Azure DevOps, Git, Maven, Docker, Kubernetes/OpenShift and Linux.
• Proactively identify opportunities to improve the maintainability, reliability, performance and observability of our services.
• Take ownership throughout the lifecycle, from initial model discussion and prototyping through implementation, production deployment and support.
• Share knowledge and contribute to a collaborative engineering and quantitative culture.
• Strong analytical and quantitative problem-solving skills combined with hands-on software engineering capability.
• Ability to understand mathematical or quantitative model specifications and translate them into reliable software implementations.
• Strong understanding of software design, maintainability, testing and production engineering.
• Ability to communicate effectively with both quantitative and technical stakeholders.
• Comfortable challenging assumptions, asking detailed questions and turning complex requirements into practical solutions.
• Strong focus on correctness, particularly where software implementations represent mathematical or financial models.
• Curiosity and willingness to develop deeper knowledge of financial markets, market data and quantitative modelling.
• Ability to work independently while collaborating effectively across different teams, disciplines and locations.
• Strong documentation skills and an ability to explain complex quantitative or technical concepts clearly.

Requirements

• Strong software engineering experience with Java and Spring Boot, including the development of production-quality backend services.
• Strong hands-on experience with Python, particularly for implementing, analysing and validating quantitative models and prototypes.
• Strong mathematical and analytical background, with the ability to understand and translate advanced mathematical formulas and quantitative model specifications into working software.
• Ability to take a model from quantitative specification and Python prototype through to a robust production Java implementation.
• Good understanding of algorithms, data structures, object-oriented design and software engineering principles.
• Experience developing automated unit, integration and regression tests, including validation of numerical results between prototype and production implementations.
• Experience working with relational databases and SQL.
• Familiarity with source control, CI/CD and documentation tooling such as Git, Maven, Azure DevOps and Confluence.
• Experience with Linux and containerized/cloud-native environments such as Docker and Kubernetes/OpenShift.
• Excellent written and spoken English.
• Ability to collaborate closely with model developers, Front Office quants and software engineers, translating between quantitative concepts and technical implementation.

Strongly preferred

• Experience as a Quant Developer, Quant Engineer or Software Engineer working closely with quantitative teams.
• Experience implementing complex mathematical or financial models in production systems.
• Knowledge of financial markets, particularly market data, pricing, valuation, proxy models or risk models.
• Experience working directly with Front Office quants and model developers.
• Knowledge of numerical methods, statistics, time series, optimization, financial mathematics or related quantitative disciplines.
• Experience comparing and validating Python reference implementations against production Java implementations, ensuring numerical consistency and correctness.
• Experience designing scalable microservices for computational or data-intensive workloads.