Job Details

Software Engineer (Python)

San Francisco • Hybrid • Full-time

About BackOps AI

BackOps AI is transforming supply chain operations with agentic AI solutions that automate complex workflows, freeing operations teams to focus on what matters most. Headquartered in the San Francisco Bay Area with flexible remote-friendly options, we foster a culture of innovation, ownership, and measurable impact.

Role Overview

As a Back-End Software Engineer (preferrably Python), you'll design, build, and operate the backend systems that power our agentic workflows-reliable services, APIs, data pipelines, and integrations that turn messy operational reality into scalable execution. You'll help shape microservices patterns, data foundations, and production reliability as we scale customers and workloads.

What You'll Do

• Microservices & APIs (Python): Design and implement backend services in Python using microservices patterns (clear service boundaries, contract-first APIs, versioning, backwards compatibility)
• Resilient Workflow Systems: Build orchestration and integration layers with strong reliability practices (idempotency, retries, rate limiting, backoff, timeouts, circuit breakers)
• Databases & Data Modeling: Design schemas, indexes, and queries; optimize performance; manage migrations; ensure consistency and data integrity across services
• Queues, Jobs, and Events: Implement background jobs, queues, and event-driven workflows; ensure safe concurrency and predictable execution
• Integrations: Build and maintain integrations with customer systems (e.g., ticketing/CRM/warehouse/shipping) and external APIs with robust error handling and observability
• POC to Production: Own backend proof-of-concepts-define requirements, implement the core, iterate quickly, and harden into production with stakeholders
• Quality, Security, and Reliability: Write secure code, review peers' changes, strengthen testing, monitoring, and incident readiness
• Operational Ownership: Participate in incident response as needed; contribute to documentation and lessons learned
• Compliance Support: Maintain confidentiality, integrity, and availability aligned with BackOps.ai information security and privacy policies, including customer data retention and deletion processes

• Experience: 4+ years building backend services in production, primarily in Python
• Python Depth: Strong experience building production APIs/services in Python; comfortable with performance tuning, profiling, and pragmatic engineering tradeoffs
• Microservices Depth: Strong understanding of distributed systems fundamentals and microservices patterns (service boundaries, dependency management, failure isolation)
• Database Expertise: Solid knowledge of relational databases (schema design, indexing, query optimization, transactions) and comfort with data modeling for evolving products
• Pragmatic Builder: Comfortable navigating ambiguity, making tradeoffs, and delivering iteratively in an early-stage environment
• Integration Mindset: Experience with external APIs and enterprise systems; strong instincts around failure modes and reliability
• Ownership & Communication: Clear written/verbal communication; able to explain architecture decisions and partner with non-technical stakeholders
• Security & Quality: Committed to secure development practices, code reviews, and maintainable code

• Node.js: Experience building backend services in Node.js (useful for interoperability with parts of our stack)
• Experience with event-driven architectures, message queues, workflow orchestration, or background job systems
• Observability experience (structured logging, tracing, metrics) and operational excellence in production
• Familiarity with SOC 2-oriented controls, data retention/deletion requirements, or regulated environments
• Experience supporting AI/agentic systems via tools/APIs, guardrails, and workflow state management

• Equity & Ownership: Competitive equity so you grow alongside the company
• Impact & Visibility: Direct access to co-founders; your work drives real operational outcomes
• Collaborative Culture: Tight-knit team of seasoned operators and AI experts
• Flexible Work: Hybrid with core Bay Area presence and remote flexibility