Lead Backend Engineer, AI Platform

Lakeview Loan Servicing•New York, NY

1d•Remote

About The Position

The Lead Backend Engineer on the AI Platform team plays a critical role in building, evolving, and leading the backend systems and engineering practices that power internal products, customer-facing capabilities, AI-enabled workflows, and operational decision-making across the organization. This role combines hands-on backend engineering depth with technical leadership, delivery ownership, and mentorship for engineers working on business-critical systems. This role contributes to the development and evolution of core backend capabilities, including service-oriented architecture, APIs, workflow orchestration, event-driven integrations, identity and permissions, and operational tooling. In addition, the Lead Backend Engineer will drive buildouts of AI application infrastructure, LLM-powered product capabilities, agentic workflows, retrieval-augmented systems, evaluation pipelines, and shared platform patterns. Success requires strong technical judgment, sound systems thinking, and the ability to guide a team toward simple, scalable, and maintainable solutions in a cloud-based, regulated, high-stakes environment. The Lead Backend Engineer is expected to operate effectively in a modern engineering environment, using automation, observability, CI/CD, testing, infrastructure-as-code, and AI-assisted development practices to deploy, manage, and improve backend systems. In parallel, this individual will help set technical direction, break down ambiguous problems, mentor engineers, raise the quality bar through design and code review, and partner closely with Product, AI, Data, Operations, and business stakeholders to build both with AI and on top of AI responsibly, securely, and pragmatically. This is a fully remote position that offers a competitive salary range of $220,000 to $300,000, plus an annual bonus. You'll also receive our excellent benefits package, which includes medical coverage starting on day one and a company-matched 401(k). Compensation may vary based on experience, location, and other job-related factors.

Requirements

5-8+ years of experience building and operating production-grade backend systems, APIs, services, or distributed applications
2+ years of experience operating in a technical lead, team lead, staff-level project lead, engineering manager, or equivalent engineering leadership capacity
Strong software engineering fundamentals, including data structures, algorithms, system design, debugging, testing, code quality, and pragmatic architecture decision-making
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
Experience building AI-enabled product capabilities on top of LLMs, foundation models, retrieval systems, embedding and search infrastructure, agent or tool-calling patterns, workflow orchestration, structured outputs, and human-in-the-loop review
Experience integrating AI capabilities with backend systems, permissions, audit trails, document workflows, data pipelines, APIs, operational decisioning, and business-critical user experiences
Demonstrated ability to use AI-assisted development tools to improve engineering velocity while maintaining code quality, security, review discipline, and accountability for technical decisions
Strong SQL skills and comfort with application data modeling, schema evolution, migrations, query performance, and data access patterns
Experience leading technical design, breaking down ambiguous problems, sequencing work, managing dependencies, and helping engineers make high-quality implementation decisions
Experience mentoring engineers through design review, code review, debugging, production support, career development, and feedback
Strong judgment in ambiguous environments where requirements evolve and systems must balance speed, reliability, security, compliance, and flexibility

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 with AI platform components such as model APIs, prompt management, embeddings, vector databases, retrieval pipelines, function calling, model routing, evaluation harnesses, and AI observability tooling
Experience improving system reliability, cost efficiency, developer productivity, team execution, and operational scalability as a platform grows

Responsibilities

Scale High-Performance Distributed Systems
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
Remain hands-on in critical areas of the codebase, especially where technical direction, architectural leverage, incident resolution, or execution speed requires senior engineering judgment
Technical Direction & Architecture
Lead the design of service architectures that support transactional, operational, analytical, and AI-driven workloads across production environments
Define practical patterns for service boundaries, idempotency, consistency, retries, failure handling, schema evolution, versioning, backward compatibility, and operational ownership
Guide technical design reviews, architecture discussions, and implementation plans to ensure systems are simple, secure, reliable, observable, and maintainable
Make sound technical tradeoffs that balance speed, simplicity, reliability, security, cost, and long-term platform leverage
AI Product & Platform Development
Lead the design and implementation of LLM-powered backend capabilities, including retrieval, tool use, workflow orchestration, structured outputs, human-in-the-loop review, evaluation, guardrails, and production monitoring
Establish patterns for integrating AI systems with core services, data stores, document workflows, permissions, audit trails, operational decisioning, and user-facing product experiences
Use AI-assisted development tools thoughtfully to accelerate software delivery while maintaining strong standards for code quality, testing, security, maintainability, and human ownership of technical decisions
Partner with AI, Data, Product, and Operations teams to translate model capabilities, business workflows, and user feedback into reliable product experiences that improve over time
Team Leadership & Delivery
Lead, mentor, and develop backend engineers through technical guidance, design feedback, code review, pairing, coaching, and clear expectations for engineering quality
Translate ambiguous business, product, and operational needs into clear technical plans, milestones, sequencing, risks, and execution paths for the team
Coordinate delivery across engineers and partner teams, helping remove blockers, manage dependencies, clarify ownership, and keep work moving with urgency and discipline
Help create a strong team culture grounded in ownership, high standards, candid feedback, pragmatic decision-making, and continuous learning
Cloud Deployment & Operations
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 tooling to proactively identify and resolve issues
Support secure, resilient, cost-conscious, and well-documented operation of cloud-based backend infrastructure and application services
Reliability, Security & Compliance
Build and lead systems with strong operational discipline, including attention to latency, availability, scalability, correctness, incident response, and production support
Implement and review 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, release readiness, and production support
Contribute to practices that support security, privacy, auditability, compliance, and risk management in a regulated environment
Cross-Functional Collaboration
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, while helping stakeholders understand technical tradeoffs and delivery risks
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, incidents, and delivery timelines