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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 $260,000 USD, 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.

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 

• 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 

Preferred Experience 

• 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 

A Note to Candidates 

You do not need prior fintech or finance experience to succeed in this role. If you are a strong backend engineering lead with solid technical judgment, a product mindset, and excitement for leading engineers through complex backend and AI-enabled systems problems, we would love to hear from you. If your background does not line up perfectly with every bullet, but this role feels like the kind of work you want to do, please apply.

Bayview is an Equal Employment Opportunity employer.  All aspects of consideration for employment and employment with the Company are governed on the basis of merit, competence and qualifications without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or any other category protected by federal, state, or local law. 

#LI-Remote

• 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 

Preferred Experience 

• Experience ...