{"id":3831893,"url":"https://alion.io/job/crisil-functional-developer","title":"Functional Developer","company":{"id":1803904,"name":"CRISIL","domain":"crisil.com","url":"https://alion.io/company/crisil","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Confluence","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Maven","optional":false},{"name":"OpenShift","optional":false},{"name":"Python","optional":false},{"name":"Spring Boot","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-11-05T19:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-05T19:35:30Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Roles and Responsibilities:\nWork closely with model developers and Front Office quants to understand new proxy model requirements and translate quantitative concepts into implementable solutions.\nContribute to the design, development and support of quantitative models, particularly models used to derive or proxy market data.\nCreate clear technical and quantitative documentation describing model assumptions, inputs, calculations, limitations and implementation decisions.\nDevelop prototype implementations to demonstrate and validate new models before production implementation.\nTranslate validated prototypes and quantitative algorithms into production-quality Java services.\nBuild scalable, resilient microservices-based applications designed to run in a cloud-native environment.\nInfluence technology choices and contribute to the architecture and design of new components and services.\nDevelop applications using modern Java and Spring Boot, following strong software-engineering practices.\nDesign and maintain automated unit, integration and regression tests, including tests that validate quantitative results.\nWork with engineers and quantitative stakeholders to ensure implementations remain consistent with their underlying model specifications.\nContribute to CI/CD and operational readiness, using technologies such as Azure DevOps, Git, Maven, Docker, Kubernetes/OpenShift and Linux.\nProactively identify opportunities to improve the maintainability, reliability, performance and observability of our services.\nTake ownership throughout the lifecycle, from initial model discussion and prototyping through implementation, production deployment and support.\nShare knowledge and contribute to a collaborative engineering and quantitative culture.\n\nKey Competencies:\nStrong analytical and quantitative problem-solving skills combined with hands-on software engineering capability.\nAbility to understand mathematical or quantitative model specifications and translate them into reliable software implementations.\nStrong understanding of software design, maintainability, testing and production engineering.\nAbility to communicate effectively with both quantitative and technical stakeholders.\nComfortable challenging assumptions, asking detailed questions and turning complex requirements into practical solutions.\nStrong focus on correctness, particularly where software implementations represent mathematical or financial models.\nCuriosity and willingness to develop deeper knowledge of financial markets, market data and quantitative modelling.\nAbility to work independently while collaborating effectively across different teams, disciplines and locations.\nStrong documentation skills and an ability to explain complex quantitative or technical concepts clearly.\n\nRequirements:\n\nEssential:\nStrong software engineering experience with Java and Spring Boot, including the development of production-quality backend services.\nStrong hands-on experience with Python, particularly for implementing, analysing and validating quantitative models and prototypes.\nStrong mathematical and analytical background, with the ability to understand and translate advanced mathematical formulas and quantitative model specifications into working software.\nAbility to take a model from quantitative specification and Python prototype through to a robust production Java implementation.\nGood understanding of algorithms, data structures, object-oriented design and software engineering principles.\nExperience developing automated unit, integration and regression tests, including validation of numerical results between prototype and production implementations.\nExperience working with relational databases and SQL.\nFamiliarity with source control, CI/CD and documentation tooling such as Git, Maven, Azure DevOps and Confluence.\nExperience with Linux and containerized/cloud-native environments such as Docker and Kubernetes/OpenShift.\nExcellent written and spoken English.\nAbility to collaborate closely with model developers, Front Office quants and software engineers, translating between quantitative concepts and technical implementation.\n\nStrongly preferred:\nExperience as a Quant Developer, Quant Engineer or Software Engineer working closely with quantitative teams.\nExperience implementing complex mathematical or financial models in production systems.\nKnowledge of financial markets, particularly market data, pricing, valuation, proxy models or risk models.\nExperience working directly with Front Office quants and model developers.\nKnowledge of numerical methods, statistics, time series, optimization, financial mathematics or related quantitative disciplines.\nExperience comparing and validating Python reference implementations against production Java implementations, ensuring numerical consistency and correctness.\nExperience designing scalable microservices for computational or data-intensive workloads.\nSkills\nBackend, Docker, Java, Kubernetes, Microservices, Python, SQL, Spring 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