Software Development Engineer III — Backend, Trust & Safety

Location: Bangalore

Experience: 6 – 8 years

About the team and the role

What we do — Trust & Safety keeps Swiggy safe on both sides: the platform stays safe for our consumers, and the platform and our businesses stay protected from fraudsters and bad actors.
What we build — the real-time risk decisioning platform that sits in the critical path of Swiggy's consumer journeys, detecting and acting on fraud and abuse as it happens, across all of our consumer businesses.
Stack — low-latency Go microservices, gRPC, event streams, NoSQL stores, caching and AWS, with Python and our data platform powering offline analysis and ML-based risk models.
Why it's hard — every decision is a two-sided bet. Act too aggressively and we hurt genuine customers; act too little and the business leaks money. And the call has to be made in milliseconds.
The adversary adapts — patterns that worked last quarter stop working, so the platform has to let risk and business teams respond quickly rather than wait on an engineering cycle.
Your charter — own architectural decisions end-to-end, lead projects independently, mentor engineers, and partner with product, analytics and business teams to translate risk and customer-experience goals into scalable, production-grade solutions.
AI-native by design — we are looking for an engineer who has already put AI systems into production at scale, who builds agentic automations to remove friction for the teams around them, and who can point AI at our own systems at scale to find where they break before an attacker does. If that is how you already work, you will have a lot of room to run here.

What will you get to do here?

Technical leadership

Create architectures and designs for new solutions across existing and new areas
Decide technology and tool choices for your team, and be responsible for them
Drive the long-term technology vision for the team
Lead code reviews, design reviews and architecture discussions, and drive engineering best practices
Experiment with new and relevant technologies, driving adoption while measuring yourself on the impact you create
Mentor engineers and raise the overall engineering bar

Building the platform

Design low-latency distributed systems that make real-time risk decisions in the critical path at Swiggy scale
Architect the signal and feature platform — streaming and batch aggregations, entity-level risk features, served within tight latency budgets
Make the platform increasingly self-serve, so risk and business teams can ship new checks and policies without an engineering deploy
Build detection for emerging abuse patterns — entity linkage, velocity and anomaly detection, and ML risk scores wired into live decisioning
Own the business outcome: measure risk exposure against false-positive impact, and build the experimentation and backtesting tooling to prove a change before it goes live
Own reliability in a critical-path service — instrumentation, observability, graceful degradation, and designing for scale from day one

What qualities are we looking for?

Engineering fundamentals

B.Tech / M.Tech in Computer Science or equivalent from a reputed college, with 6 – 8 years of experience in a product development company
Sound knowledge and application of algorithms and data structures, with space and time complexities
Proficiency in Go or Python (ideally both)
Follows industry coding standards, writing maintainable, scalable and efficient code to solve business problems

Systems and scale

Strong experience building and operating distributed systems — message queues, event-driven architectures, stream processing, caching, async processing
Solid system design skills, comfortable owning end-to-end architecture
Experience building latency-sensitive services in a transactional critical path — p99s, timeouts, circuit breaking, fallback behavior
Experience with NoSQL databases, Kafka or similar streaming systems, and AWS
Comfort working with data — SQL on a warehouse / data lake, building metrics, and reasoning about decisions from production data rather than intuition

Leadership

Demonstrated ability to lead projects independently from design to production
Track record of mentoring engineers and elevating team capabilities

What "AI-native" means for this role

Has productionised AI solutions at scale, not just prototypes
Builds automations for real user workflows — spotting where people lose time to manual, repetitive steps and engineering that friction away
Hands-on with open-weight models such as Qwen or GLM to build task-specific SLMs using few-shot prompting
Proven experience in loop engineering and quality gates — designing the agentic loops, evals and automated checks that make AI output trustworthy enough to ship
Can break a system using AI at scale — adversarial testing as a first instinct
Active user of AI coding tools (Claude, Copilot, Cursor) in day-to-day engineering

Worked on trust & safety, fraud, risk or payments systems, with an instinct for how legitimate flows get exploited (ethical hacking exposure is a plus)
Experience with rule engines, policy / decisioning platforms, or configuration-driven systems built for non-engineering users
Hands-on experience productionising ML — feature stores, real-time model serving, model monitoring
Exposure to graph-based detection (entity resolution, linkage analysis) or anomaly detection at scale

Visit our tech blogs to learn more about some of the challenges we deal with:

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, disability status, or any other characteristic protected by the law.

Software Development Engineer III — Backend, Trust & Safety

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