Backend Engineer

Lakeview Loan Servicing•Toronto, ON

1d•Remote

About The Position

The Backend Engineer on the AI Platform team builds and evolves backend systems powering internal products, customer-facing capabilities, AI-enabled workflows, and operational decision-making. The role designs, builds, tests, and operates reliable, scalable, and flexible services across internal and external use cases, including service-oriented architectures, APIs, workflow orchestration, event-driven integrations, identity and permissions, operational tooling, and system interfaces. The position contributes to core backend and distributed systems capabilities in a cloud-based, regulated, high-stakes environment. It uses automation, observability, CI/CD, testing, and infrastructure-as-code to deploy, manage, and improve production services, while enabling product, data, AI, reporting, and operational workflows through trustworthy, maintainable, and performant backend services. This is a fully remote role.

Requirements

3–5+ years of experience building and operating production-grade backend systems, APIs, services, or distributed applications.
Strong software engineering fundamentals, including data structures, algorithms, system design, debugging, testing, and code quality.
Experience designing, building, maintaining, and debugging production services that support real users or business-critical workflows.
Experience with modern backend programming languages such as Python, Go, C++, Rust, Java, Kotlin, Scala, TypeScript, or C#.
Experience with API design, service boundaries, event-driven or asynchronous architectures, relational data stores, and non-relational data stores.
Experience building transactional systems where correctness, idempotency, consistency, reconciliation, and auditability matter.
Experience deploying and operating backend services on AWS, GCP, Azure, or another major cloud platform.
Strong SQL skills and comfort with application data modeling, schema evolution, migrations, query performance, and data access patterns.
Experience building AI-powered capabilities on top of LLMs, including orchestration, retrieval, evaluation, tool integrations, and workflow automation.
Comfort working with CI/CD, infrastructure-as-code, observability, incident response, and production operations for backend systems.
Strong judgment in ambiguous environments where requirements evolve and systems must balance speed, reliability, security, and flexibility.
Clear communication skills with technical and non-technical teammates.

Nice To Haves

Experience in fintech, mortgage, lending, payments, insurance, banking, capital markets, or other regulated domains.
Experience with queues, streaming, and event-driven platforms such as Kafka, Kinesis, SQS/SNS, Pub/Sub, RabbitMQ, or similar systems.
Experience building secure identity, access control, permissions, audit trail, policy, or compliance-oriented backend capabilities.
Experience building high-volume, low-latency, multi-tenant, B2B, enterprise, or internal platform systems.
Experience building AI products, developer platforms, workflow automation systems, or LLM-powered enterprise applications.
Experience improving system reliability, cost efficiency, developer productivity, and operational scalability as a platform grows.

Responsibilities

Design, build, and maintain production backend services for internal and external use cases, including product workflows, operational tools, integrations, APIs, and AI-enabled applications.
Develop understandable, testable, operable, and extensible APIs, domain models, service interfaces, and business logic.
Build scalable backend workflows for business processes involving loans, documents, accounts, users, permissions, vendors, and operational decision-making.
Ensure services remain reliable, testable, observable, and resilient as business needs, data volumes, and product surfaces evolve.
Contribute to service architectures that support transactional, operational, analytical, and AI-driven production workloads.
Integrate internal systems, third-party platforms, vendor APIs, databases, files, message queues, event streams, and cloud services.
Design patterns for service boundaries, idempotency, consistency, retries, failure handling, schema evolution, versioning, and backward compatibility.
Help define architecture standards balancing speed, simplicity, reliability, security, and maintainability.
Deploy, operate, and improve backend services on major cloud platforms.
Use infrastructure-as-code, CI/CD, automated testing, and deployment automation to improve release speed, consistency, and reliability.
Monitor production services with logging, tracing, metrics, alerting, and observability tools; proactively identify and resolve issues.
Support secure, resilient, and cost-conscious operation of cloud-based backend infrastructure and application services.
Build systems with operational discipline and attention to latency, availability, scalability, correctness, incident response, and production support.
Implement authentication, authorization, permissions, audit logging, data protection, and secure service-to-service communication.
Establish and maintain standards for API documentation, service ownership, runbooks, operational metrics, change management, and production readiness.
Contribute to security, privacy, auditability, compliance, and risk-management practices in a regulated environment.
Partner with Product, Engineering, Data, AI, Design, Operations, and business stakeholders to understand workflows, user needs, constraints, and priorities.
Translate business and operational requirements into clean, scalable, maintainable, and secure backend solutions.
Support downstream consumers of backend capabilities, including product teams, analysts, researchers, AI systems, operational users, and external integrations.
Communicate system behavior, tradeoffs, risks, dependencies, and delivery timelines to technical and non-technical stakeholders.
Continuously improve service performance, reliability, scalability, security, and developer productivity.
Identify opportunities to simplify architecture, reduce operational toil, improve shared platform leverage, and make systems easier to reason about.
Deliver iteratively, moving from problem definition to implementation, validation, and improvement.
Improve engineering quality through thoughtful design, code review, testing, documentation, mentorship, and operational discipline.