Backend Engineer

Lakeview Loan Servicing•New York, NY

2d•Remote

About The Position

The Backend Engineer on the AI Platform team plays a critical role in building and evolving the backend systems that power internal products, customer-facing capabilities, AI-enabled workflows, and operational decision-making across the organization. This role is responsible for designing, building, testing, and operating reliable, scalable, and flexible services that support a wide range of internal and external use cases. This role contributes to the development and evolution of core backend and distributed systems capabilities, including service-oriented architectures, APIs, workflow orchestration, event-driven integrations, identity and permissions, operational tooling, and system interfaces that enable product, data, and AI teams to move quickly. Success requires strong technical depth, sound systems thinking, and the ability to build dependable solutions in a cloud-based, regulated, high-stakes environment. The Backend Engineer is expected to operate effectively in a modern engineering environment, using automation, observability, CI/CD, testing, and infrastructure-as-code practices to deploy, manage, and improve production services. In parallel, this individual will help enable downstream product capabilities, operational workflows, reporting, and AI systems by ensuring that backend services are trustworthy, maintainable, and performant.

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 services that run in production and 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 major cloud platforms such as AWS, GCP, or Azure
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 both 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 a wide variety of internal and external use cases, including product workflows, operational tools, integrations, APIs, and AI-enabled applications
Develop well-structured APIs, domain models, service interfaces, and business logic that are easy to understand, test, operate, and extend
Build scalable backend workflows that support complex business processes across loans, documents, accounts, users, permissions, vendors, and operational decision making
Ensure backend services are 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 workloads across production environments
Integrate with 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 practical architecture standards that balance speed, simplicity, reliability, security, and long-term maintainability
Deploy, operate, and improve backend services on major cloud platforms such as AWS, GCP, or Azure
Use infrastructure-as-code, CI/CD, automated testing, and deployment automation to improve release speed, consistency, and reliability
Monitor production services using logging, tracing, metrics, alerting, and observability tools to proactively identify and resolve issues
Support secure, resilient, and cost-conscious operation of cloud-based backend infrastructure and application services
Build systems with strong operational discipline, including 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 patterns
Establish and maintain standards for API documentation, service ownership, runbooks, operational metrics, change management, and production readiness
Contribute to practices that support security, privacy, auditability, compliance, and risk management in a regulated environment
Partner closely with Product, Engineering, Data, AI, Design, Operations, and business stakeholders to understand workflows, user needs, constraints, and delivery 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 clearly with both technical and non-technical stakeholders about system behavior, tradeoffs, risks, dependencies, and delivery timelines
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
Operate with a strong bias toward action and iterative delivery, moving quickly from problem definition to implementation, validation, and improvement
Help raise the bar on engineering quality through thoughtful design, code review, testing, documentation, mentorship, and operational discipline