Parallel Partners

Backend Engineer

• 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.

AI Backend Engineer

Android Engineer

• Build and maintain production-grade Android applications using Kotlin.
• Design and implement intuitive, responsive mobile interfaces for insurance, payments, and customer workflows.
• Integrate backend APIs for quotations, policy management, claims, and transactional services.
• Contribute to mobile features that incorporate AI-assisted capabilities (e.g., chat experiences, recommendations, automation flows).
• Optimize application performance, responsiveness, battery usage, and memory efficiency across devices.
• Implement analytics, crash reporting, and user behavior tracking to improve product quality.
• Collaborate closely with backend, product, design, and AI/ML teams throughout the development lifecycle.
• Ensure stability, scalability, and security across production mobile systems.
• Continuously improving engineering standards, architecture, and mobile best practices.
• Deliver stable, high-performance Android experiences used at scale.
• Improve app reliability, responsiveness, and efficiency across devices.
• Reduce crashes and production incidents through better engineering practices.
• Ship mobile features confidently with strong system design and testing discipline.
• Enable AI-assisted product capabilities through mobile integration where applicable.
• Contribute to a scalable and maintainable mobile architecture.

Android Software Engineer

AI Engineer

• Build AI-powered product features integrated into real insurance workflows.
• Design and optimize LLM-based interactions across customer and internal systems.
• Develop prompts, reasoning flows, and structured AI outputs for production use cases.
• Evaluate model performance using real-world data, feedback loops, and experiments.
• Improve reliability of AI outputs through guardrails, fallback logic, and validation layers.
• Work closely with backend engineers to integrate AI capabilities into APIs and workflows.
• Design and implement AI-driven automation features (chat, recommendations, decision support).
• Analyze user behavior and model responses to continuously improve AI performance.
• Collaborate with product, design, and engineering teams to ship end-to-end AI features.
• Contribute to AI system design decisions focused on usability and real-world impact.

Applied AI Engineer

Full-stack Engineer

• Build and ship iOS features using Swift.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable and reliable.
• Improve performance, crash rate, loading states, responsiveness, memory usage and battery efficiency.
• Use analytics, user behavior and production issues to improve the product.

iOS Software Engineer

• Lead the design and development of production AI systems powering insurance workflow automation.
• Architect AI orchestration layers connecting LLMs, backend services, and business workflows.
• Own end-to-end AI system design, including inference pipelines, routing, caching, and fallback strategies.
• Drive engineering decisions around latency, reliability, cost, and scalability of AI services.
• Lead implementation of Observability Systems (logging, monitoring, tracing, alerting).
• Review and guide backend AI implementation across engineering teams.
• Collaborate with product, backend, DevOps, and operations teams to ship end-to-end AI features.
• Debug complex production issues across distributed AI systems and lead root-cause analysis.
• Define engineering standards and best practices for AI system development.
• Mentor engineers and elevate technical execution quality across teams.

AI Backend Engineer

Android Software Engineer

Applied AI Engineer

iOS Software Engineer

Lead AI Engineer