Binariks is looking for a Senior/Staff Product Engineer to take end-to-end ownership of a correctness-critical product — from back-end processing and data modeling through external integrations to the user-facing interface.
The product involves asynchronous and long-running workflows, multi-tenant data, third-party and AI/LLM integrations, and complex business rules where correctness and traceability are critical. This is a highly autonomous, client-facing role: the engineer will work directly with stakeholders to clarify requirements, challenge assumptions, prototype business flows, and drive technical decisions from ambiguity through production.
What We’re Looking For: Must skills: Candidate must demonstrate hands-on production experience.
~8+ years of experience building production systems, including ownership of at least one product/system end to end.
Strong experience designing asynchronous and/or long-running workflows, including idempotency, retries, and safe recovery from partial failures.
Strong full-stack engineering capability across backend and frontend.
Systems decomposition experience: designing, splitting, or consolidating services/modules and understanding the trade-offs of architectural boundaries.
Multi-tenancy and authorization: tenant isolation, least privilege, delegated administration, and auditability.
Production-grade observability and operability: distributed/cross-service tracing, metrics, error reporting, and cost awareness.
Data modeling and safe schema evolution, including forward-only migrations.
Testing across layers, including validation of user-visible behavior rather than relying exclusively on unit/API-level testing.
Experience integrating unreliable third-party services using adapters, caching, timeouts, cost controls, and degraded-mode strategies.
Application security fundamentals, including trust boundaries, credential management/rotation, injection/SSRF risks, and protection of client-side secrets.
Strong verification discipline and ability to validate data against the actual source of truth.
Strong written technical communication: architecture decision records, trade-off documentation, and runbooks.
Product judgment and ability to identify when technically valid outputs could nevertheless mislead users.
Proven client-facing ownership: comfortable leading requirements conversations rather than only implementing predefined tickets.
Ability to uncover underlying business rules, identify contradictions, prototype business flows, and secure stakeholder agreement.
Strong critical thinking, stakeholder communication, autonomy, and ability to make progress with incomplete requirements.
Strongly preferred: Significantly strengthens a candidate; gaps here are acceptable if the required skills are strong.
Applied AI/LLM integration experience, particularly structured outputs, evaluation, and cost control.
Experience delivering on-premise or air-gapped solutions alongside hosted/cloud products.
Enterprise SSO and federated identity experience.
Experience across different deployment topologies.
Experience in regulated or other high-stakes domains.
Productive, accountable use of AI coding agents in software development.
Nice to Have: Bonus context. We do not expect any single candidate to cover these. Any of the following would materially reduce our risk, so please call out candidates who have even partial exposure.
Experience working with AI/ML-enabled products without necessarily having an ML research or data-science background.
Exposure to multiple languages, frameworks, and cloud environments, demonstrating the ability to become production-ready in unfamiliar technologies quickly.
Experience working both as part of a product engineering team and as the sole engineer/owner of an independent workstream.
Your Responsibilities:
Design and maintain long-running, resumable, multi-stage workflows, including idempotency, retries, partial-failure recovery, and bounded
