AI Backend Engineer (AI Workflow Systems)

About the Role

The AI Backend Engineer will be part of a fast-paced, globally distributed team, working closely with frontend, product, operations, and ML teams to ship end-to-end features. The ability to collaborate effectively, think critically, and communicate complex technical concepts is crucial in this role. With a strong ownership mindset and a bias toward shipping, you will be expected to continuously improve system reliability, scalability, and performance.

Bjak's commitment to using AI to simplify insurance and financial services for millions of users is an attractive prospect for those looking to make a real impact. The company's focus on technical excellence, speed of execution, and practical decision-making creates an environment where engineers can thrive.

What You Will Do

• Build and operate backend systems that serve AI-powered insurance workflows in production
• Design and implement AI orchestration layers that connect models, APIs, workflows, and business logic
• Build inference pipelines for LLM-based and AI-assisted automation systems
• Optimize latency, throughput, and cost across AI services (caching, batching, streaming, routing)
• Design stable service boundaries between backend systems, ML components, and product APIs
• Implement observability: logging, metrics, tracing, alerting, and incident response workflows
• Debug production issues across distributed AI systems and resolve root causes
• Collaborate closely with frontend, product, operations, and ML teams to ship end-to-end features
• Continuously improve system reliability, scalability, and performance

What We Are Looking For

• Strong backend engineering experience in production systems
• Experience building or operating high-throughput, low-latency services
• Familiarity with AI systems (LLMs, embeddings, or AI workflows)
• Experience with distributed systems and production debugging
• Strong understanding of APIs, data flows, and system design principles
• Experience with observability tools (logging, monitoring, tracing)
• Strong ownership mindset and bias toward shipping
• Comfortable working in fast-paced, globally distributed teams
• Familiarity with Python, Node.js, and SQL/NoSQL databases
• Experience with Kubernetes, Docker, and distributed systems tooling

Nice to Have

• Experience with LLM APIs (OpenAI / Anthropic / open-source models)
• Knowledge of insurance and financial services industry
• Familiarity with modern engineering practices and scalable systems
• Experience with cloud-based services and serverless architectures

Benefits and Perks

• Attractive salary package
• Opportunities for career growth and professional development
• Fully remote work arrangement with globally distributed teams
• Learning and development budget for continuous growth
• High ownership culture with end-to-end ownership of engineering systems
• Modern engineering practices with a focus on scalability and reliability
• Competitive compensation and benefits package
• Access to cutting-edge technologies and tools
• Collaborative and dynamic work environment

• Tip: Showcase your experience with AI systems, including LLMs, embeddings, or AI workflows, to stand out in the application process.
• When applying, highlight your ability to work collaboratively with cross-functional teams, including frontend, product, operations, and ML teams.
• Be prepared to discuss your understanding of system design principles, APIs, and data flows, as well as your experience with observability tools.
• To demonstrate your strong ownership mindset, provide examples of how you have taken ownership of systems and features in previous roles.
• When negotiating salary, consider the company's attractive compensation package and opportunities for career growth and professional development.
• Be cautious of red flags such as unclear expectations, lack of communication, or unrealistic goals, and be prepared to ask questions during the interview process.
• To increase your chances of success, make sure to showcase your experience with Python, Node.js, and SQL/NoSQL databases, as well as your familiarity with Kubernetes, Docker, and distributed systems tooling.