About the role
- Core AI-driven systems for risk scoring, delay prediction, and document generation
- Real-time tracking systems for vehicles, ETAs, and routing
- Credit and underwriting pipelines that run continuously on high value enterprise deals.
- Applications used daily by thousands of vendors and customers
- Automated generation of documentation, audit trails and smart contracts.
- You’ll work in a small team with a strong focus on shipping, ownership, and scalability.
- Lead the design and implementation of complex, high-impact features
- Assist in architectural decisions around scalability, reliability, and system boundaries
- Help shape how AI and data systems are integrated into production workflows (latency, observability, failure handling)
- Mentor junior engineers through code reviews, design discussions, and day-to-day guidance
- Drive best practices around system design, code quality, testing, and observability
- Improve performance and reliability of real-time systems such as tracking, analytics, and automated documentation
- Take ownership of services in production - you build it, ship it, monitor it, and improve it
- 5-8 years of experience building backend systems in production environments
- Strong expertise in system design, distributed systems, and scalable architectures
- Proven ability to write clean, maintainable, and efficient code
- Deep understanding of engineering practices, tooling, CI/CD, and operational readiness
- Strong communication skills and the ability to influence technical decisions
- Experience improving performance, reliability, and observability of live systems
- Deliver critical projects with high quality and predictable timelines
- Act as a force multiplier by improving team productivity and engineering standards
- Collaborate closely with product, operations, and leadership to solve real business problems
- Balance technical depth with pragmatism in a fast-moving environment
- Engineering with real impact: Your work directly affects logistics, payments, and compliance
- Serious backend problems: Systems handle real transactions, not demo workloads
- Practical AI: Used in pricing, credit, logistics, and fraud - not just experiments
- Scalable engineering practices: CI/CD, infra-as-code, design reviews