Lead Backend Engineer, AI Platform

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
• 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
• 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
• Carefully 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

Qualifications

• 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
• 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