1 day ago
Bengaluru, IndiaSenior
Responsibilities
- Design architectures and low-latency distributed systems for real-time fraud and abuse decisioning.
- Architect streaming and batch feature platforms, entity-level risk features, and ML-powered live decisioning.
- Build self-serve rule, policy, and decisioning capabilities for risk and business teams.
- Develop entity-linkage, velocity, anomaly-detection, experimentation, and backtesting tooling.
- Own reliability for critical-path services through instrumentation, observability, graceful degradation, and scalability.
- Lead projects from design through production, make technology choices, and drive the team’s long-term technical vision.
- Lead code and design reviews, establish engineering practices, and mentor engineers.
- Productionize AI solutions, agentic workflows, task-specific small language models, evaluation loops, automated quality gates, and adversarial testing.
- Productionize ML through feature stores, real-time model serving, and model monitoring.
Requirements
- B.Tech or M.Tech in Computer Science or equivalent, with 6–8 years of experience in a product development company.
- Strong knowledge of algorithms and data structures, including space and time complexity.
- Proficiency in Go or Python, ideally both, and ability to write maintainable, scalable, and efficient code.
- Strong experience building and operating distributed systems, message queues, event-driven architectures, stream processing, caching, and asynchronous processing.
- Solid system-design skills and experience owning end-to-end architecture for latency-sensitive transactional services.
- Experience with NoSQL databases, Kafka or similar streaming systems, AWS, and SQL-based analysis on a warehouse or data lake.
- Demonstrated ability to lead projects independently and mentor engineers.
- Experience productionizing AI solutions at scale, building automations, and working with open-weight models such as Qwen or GLM using few-shot prompting.
- Experience designing agentic loops, evaluations, automated checks, and quality gates for trustworthy AI output.
- Experience with trust and safety, fraud, risk, or payments systems; ethical hacking exposure is a plus.
- Experience with rule engines, policy or decisioning platforms, or configuration-driven systems for non-engineering users.
- Hands-on experience productionizing ML, including feature stores, real-time model serving, and model monitoring.
- Exposure to graph-based detection, entity resolution, linkage analysis, or anomaly detection at scale.
- Experience using Claude, Copilot, or Cursor in day-to-day engineering is expected.
Tech Stack
Categories
About Swiggy
Swiggy builds an app-based marketplace for on-demand convenience in India, connecting consumers with restaurants, groceries, and pick-up/drop services. It runs Swiggy Food and Instamart, plus offerings like Dineout and Genie, and monetizes via delivery fees, restaurant commissions, and the Swiggy One subscription. Founded in 2014 and headquartered in Bengaluru, it serves 600+ Indian cities.