Nuvama's Quant Engineering team develops systematic trading strategies and the infrastructure to research, test, and deploy them across Indian financial markets. The team operates at the intersection of quantitative finance, software engineering, and applied AI — building tools and models that are used in live trading.
What You'll Work On
- Backtesting infrastructure: building and improving the internal backtesting platform — data pipelines, execution simulation, performance reporting, and tooling that quant researchers use daily.
- Data engineering: building reliable pipelines for tick, minute, and daily market data (equities, futures, options) — ingestion, validation, storage, and serving to downstream consumers.
- AI engineering support: contributing to internal LLM-powered tooling — building APIs, integrating model outputs into workflows, and maintaining the infrastructure that connects AI capabilities to the research platform.
- Algorithmic execution engineering: implementing and testing order routing logic, execution simulation, and latency measurement tooling that underpins live systematic trading.
- Performance and monitoring tooling: building dashboards, metrics, and diagnostic tools that give researchers and traders visibility into platform health, backtest results, and live system behaviour.
What We're Looking For
- Pursuing B.Tech / M.Tech in CS, EE, or a related engineering discipline from a Tier 1 institution.
- Strong Python fundamentals — comfortable writing clean, testable code and working with numerical data at scale (pandas, numpy).
- Understanding of software engineering fundamentals: data structures, algorithms, API design, and working with databases (SQL).
- Ability to take a quantitative specification from a researcher and translate it into working, reliable code — bridging the gap between math and production systems.
- Basic familiarity with financial data types — OHLCV bars, option chains, market events — enough to work with them programmatically without hand-holding.
- Comfortable working on Linux servers, using git, reading logs, and debugging in a distributed environment.
- Genuine interest in building systems that operate in a live financial environment — correctness, reliability, and performance all matter.
Nice to Have
- Prior internship or project experience building data pipelines, financial tooling, or trading-related infrastructure.
- Familiarity with Indian market structure — F&O expiry conventions, settlement cycles, margin frameworks.
- Exposure to statistical libraries: scipy, statsmodels, or similar.
- Interest or experience in applying LLMs or ML to financial research problems.
- Open-source contributions or personal projects demonstrating engineering initiative — GitHub activity, side projects, or hackathon work.
- Access to high-quality market data across equities, futures, and options.
