Machine Learning Engineering Manager Fraud Detection in Gurugram

Who we are Graviton Research Capital is a privately funded quantitative trading firm. We trade across a multitude of asset classes and trading venues using a diverse range of concepts, from time series analysis and stochastic models to machine learning and statistical inference. We analyse terabytes of data to identify pricing anomalies and drive innovation in financial markets. Role Overview We are looking for a Program Manager who thrives at the intersection of rigorous engineering and predictable delivery. You will not just "manage tasks" — you will orchestrate the development lifecycle for mission-critical systems. Your goal is to ensure that our elite engineering teams can focus on high-performance code while you own the execution strategy, dependency mapping, and release discipline. Key Responsibilities Lead Agile ceremonies (Sprint Planning, Stand-ups, Retrospectives) tailored for deep-tech engineering teams. Transform high-level trading requirements into granular, executable backlogs. Own capacity planning and burn-down metrics to provide high-visibility delivery timelines. Navigate the complex interplay between engineering teams (e.g., Connectivity, Core Infrastructure, Simulation) to prevent bottlenecks. Build and maintain advanced Jira dashboards, automated roadmaps, and Confluence documentation that serve as the "single source of truth" for stakeholders. Proactively identify technical debt, architectural blockers, or resource gaps that threaten release stability. Continuously refine Agile methodologies to suit low-latency, performance-sensitive development cycles (where "Definition of Done" includes rigorous performance benchmarking). Eligibility & Required Skills 5+ years of experience as a TPM, Program Manager, or Scrum Lead in a product-engineering environment (HFT, FinTech, Networking, or Kernels/Systems). A strong grasp of the software development lifecycle for high-performance systems. While you won't write code, you must understand concepts li

About Us: Sauce Labs is the world’s largest full-lifecycle, test automation platform, and the company behind Selenium. Trusted by 80% of the world’s top ten largest financial institutions and over 300,000 enterprise users, Sauce Labs provides the only AI platform capable of turning business intent into autonomous testing and quality assurance. With a proprietary dataset of 8.7 billion test runs, Sauce Labs empowers the Fortune 2000 to bridge the gap between AI-driven code generation and enterprise-grade software quality. Learn more at saucelabs.com . The Role: We are seeking an innovative and experienced AI Architect to join our engineering leadership team. This is a strategic role that will be instrumental in designing and building the next generation of AI-powered features for our continuous testing platform. You will be responsible for architecting scalable and robust AI solutions that transform how our customers gain insights from their test data and production environments, and how they create tests. Responsibilities: Define AI Architecture: Lead the design and architecture of cutting-edge AI/ML solutions for new product offerings, ensuring scalability, performance, quality and reliability within a cloud-native environment. AI-Powered Insights (Test & Production): Architect AI systems to derive actionable insights from vast quantities of test run logs and analytics data. This includes identifying patterns, anomalies, and performance trends. Production Error Reporting Integration: Design AI solutions that integrate with our existing error reporting product to analyze production issues for mobile and web applications, providing deeper understanding and predictive capabilities. Unified Data Intelligence: Develop architectures for combining insights from both test runs and production data, creating a holistic view of application quality and user experience. Automated Failure Analysis & Remediation: Architect AI models and systems t

Role: Application Reliability Engineer Location: Gurgaon Who we are Graviton Research Capital is a privately funded quantitative trading firm striving for excellence in financial markets research. We trade across a multitude of asset classes and trading venues using a diverse range of concepts, from time series analysis and stochastic models to machine learning and statistical inference. We analyse terabytes of data to identify pricing anomalies and drive innovation in financial markets. Key Responsibilities and Deliverables The ideal candidate will possess a strong background in technical support, with a passion for problem-solving and a commitment to excellence. As an Application Reliability Engineer, you will be responsible for: Monitor production services and respond quickly to alerts, incidents, and outages to ensure smooth operation and minimal downtime. Monitor trading systems and infrastructure., Triage issues across trading support services, databases, and infra; escalate and coordinate with the right owners, and drive root-cause analysis and ensure fixes are implemented for long-term stability. Serve as the first line of defense for trading operations. Proactively identify, address recurring issues, and build automation to reduce manual intervention. Improve observability by enhancing monitoring, logging, and alerting systems. Develop and maintain operational runbooks and SLO/SLA metrics. Eligibility and Required Skills Possess a degree in a highly analytical field, such as Engineering, or Computer Science 2-5 years of experience in Python, Shell/Bash scripting. Experience with Linux and shell/bash online tools. Hands-on experience with databases (SQL, NoSQL) Strong problem-solving and analytical skills Excellent communication skills Ability to remain calm and analytical under production pressure Good to have: Familiarity with monitoring/alerting stacks (Prometheus, Grafana, ELK, etc.) Familiarity with distributed messaging (Kafka) and caching systems (Red

Who are we: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets research. We trade across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition, to statistical inference analyzing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. As part of this team you will be tasked to apply machine learning and specifically deep learning techniques to trading problems while staying connected to broader research community. The researcher will put theory into practice and can immediately impact the global trading landscape with the expanding presence of Graviton in various markets. Description Lead research in applying machine learning to a wide variety of datasets and trading problems Follow latest developments in academic research and incorporating research techniques from different fields of applications to our problems Improve tick-by-tick order book based time series feature sets using latest preprocessing techniques Work on current and develop new deep learning models to exploit large pool of in-house features and computing infrastructure Develop scalable pipeline for building predictive models across global markets Discover and implement new sources of predictive alpha, verify that they improve existing models, and integrate them into the firm's strategy development pipeline Partner with quant researchers and software developers in implementation of conducted research to production using Python / C++ Advise infrastructure support team on latest developments on hardware and software to improve computing infrastructure for ML based research Qualifications Masters or PhD in Computer Science, Mathematics, Statistics, or a related field At least two years of demonstrated experience of ML/AI research in a professional setting or at a repu

Quantitative Researcher Every trading strategy begins with a question worth answering. At Graviton, we believe the best trading strategies don't come from following established playbooks. They come from asking better questions. Why does a pattern exist? What is driving it? Is it real or just noise? Can it be explained, modelled and turned into an edge? As a Quantitative Researcher , you'll work on these questions every day. You'll investigate market behaviour, develop quantitative models, build and test hypotheses and turn insights into trading strategies. You'll work alongside quantitative researchers and technologists in an environment where research moves quickly from an idea on a whiteboard to something that can influence live trading. The problems are open-ended. The data is enormous. The answers aren't in a textbook. You'll be expected to find them. What You'll Work On You'll work across different areas of quantitative research and systematic trading. Depending on your team and research interests, your work may include: Discovering predictive patterns and sources of alpha from billions of market events . Formulating hypotheses about market behaviour and designing experiments to test them. Applying probability, statistics, optimization and machine learning to complex research problems. Building predictive models and quantitative signals for systematic trading. Developing and improving research infrastructure that enables faster experimentation and deeper analysis. Working with large and complex datasets to uncover patterns that aren't immediately visible. Designing robust backtests and statistical tests to distinguish genuine signals from noise. Investigating market microstructure and understanding how markets behave at different timescales. Evaluating strategy performance, identifying weaknesses and continuously refining models. Working closely with technologists to translate research ideas into efficient, production-ready systems. Building AI-powered r