Senior Backend Engineer

Role overview A senior backend engineering role on a cross-functional team working across data platform architecture and distributed integrations that power AI-driven internal workflows. The position blends hands-on coding with technical leadership, observability, and mentorship in an AI-native, fast-moving SaaS environment. The stack includes Python, GoLang, TypeScript/React, MongoDB, Elasticsearch, Redis, Kafka, Kubernetes/Docker, GCP, Terraform/Ansible, and GitHub Actions.

Responsibilities - Design and ship highly scalable backend services, data pipelines, and integration endpoints that perform reliably at high volume - Gather requirements, author design documents, and drive technical discussions to align cross-functional stakeholders - Lead the full software development lifecycle, from requirements and architecture through QA and ongoing maintenance - Build systems with observability as a first-class concern, including monitoring, alerting, and dashboards - Communicate development progress clearly to product leads, engineering managers, and other partners - Apply AI tools across coding, debugging, review, and documentation as a natural part of the workflow - Navigate ambiguity, unblock projects, and maintain delivery cadence on large initiatives

Requirements - At least 5 years of industry backend development experience, including 1–2 years at a senior level on large-scale applications - Experience developing distributed web applications at scale - Background in database modeling with SQL or NoSQL systems - Hands-on design and use of RESTful services - Strong computer science fundamentals in data structures, algorithms, and object-oriented design - Track record of leveraging AI tools to accelerate development workflows - Experience in fast-scaling startup or high-velocity environments

Nice to have - BS or MS in Computer Science, Computer Engineering, or a related field - Hands-on work with GCP, Kubernetes, Docker, Mongo, Redis, Ruby on Rails, microservices, or machine learning

Benefits and work setup - AI-native culture with continuous improvement and learning expectations - Resources, support, and autonomy to own meaningful work - Strong cross-department collaboration and shared ownership model - Encouragement to experiment, take smart risks, and pursue bold ideas