Principal Software Engineer, Bangalore

Set the code quality bar within the Platform Team — write clean, maintainable, well-structured code that others reference as the standard.

Drive test coverage as a first-class engineering concern — unit, integration, and contract tests are part of delivery, not an afterthought.

Instrument services for observability — structured logging, distributed tracing, and health metrics built in from the start.

Identify and address technical debt proactively — does not wait to be asked to flag quality issues or design debt in owned services.

Contribute to low-level design discussions — bring clear reasoning on component design, data model trade-offs, and API contract decisions.

Support and mentor Senior Engineers on the Platform Team — review code with depth, share knowledge, and raise the team’s technical level through example.

Collaborate with the Platform Software Architect on implementation decisions — understands where the Architect’s design direction is heading and builds toward it.

Production-grade Python backend engineering using FastAPI — owns implementation decisions independently, sets the quality bar, and requires no guidance on complex backend problems.

Low-level system design — designs individual components, data structures, service internals, and API contracts from first principles. Can articulate and defend trade-offs with depth and clarity.

Event-driven architecture — building services that produce and consume events correctly. Message ordering, delivery guarantees, idempotency, and error handling in event-driven systems.

Immutable data design and append-only patterns — designing data models where every state change is traceable and tamper-evident. The engineering foundation for compliance-grade platform capabilities.

Multi-tenant data patterns — tenant-scoped data access, row-level isolation, and per-tenant configuration at the service level.

API design and OpenAPI contracts — designing and versioning platform APIs that downstream consumers can build against reliably.

SQL and NoSQL databases at production scale — schema design, query optimisation, indexing strategies, and knowing when to use each.

Kubernetes, Docker, and Helm — deploying and releasing containerised services in production, including CI/CD pipeline integration. Code quality: maintainable, readable, well-tested code with error handling, observability instrumentation, and service resilience patterns.

API gateway concepts — how platform services are exposed through a gateway layer. Sufficient to design services that integrate correctly without owning the gateway.

Security and IAM patterns — OAuth flows and token propagation at the service level. Platform Architect owns security architecture depth. LLM tooling concepts: what the platform must support for agents to operate on it.

Experience building audit or compliance-grade systems — immutable logs, decision trails, tamper-evident data.

Workflow orchestration tools — Temporal or equivalent, learnable on the job. AI-native SDLC: familiarity with Human-led Agentic SDLC practices.

Azure cloud experience.

End-to-end ownership of backend services on a foundational platform that every AI agent team builds on — with full accountability for reliability, observability, and production-readiness of the infrastructure you ship.

Deep hands-on experience in event-driven architecture, immutable data design, and multi-tenant patterns in a production context where correctness, compliance, and scalability are all non-negotiable simultaneously.

Exposure to building audit and compliance-grade infrastructure in a SOX-governed environment — learning to design systems where every state change is traceable, tamper-evident, and queryable under enterprise audit requirements.

Close collaboration with the Platform Software Architect on implementation decisions, and the opportunity to mentor Senior Engineers — the Principal level is where individual technical excellence directly shapes the quality standards of the broader team.

If you are an engineer who owns their implementations completely, sets quality standards that others follow, and is ready to build the infrastructure foundation that an enterprise AI platform depends on — we encourage you to apply.

At our core, Trintechers stand committed to fostering a culture rooted in our core values – Humble, Empowered, Reliable, and Open. Together, these values guide our actions, define our identity, and inspire us to continuously strive for excellence in everything we do.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin or disability.