Lead Backend Engineer, AI Platform
The Judge Group
Software Engineering, Data Science
Remote
Posted on May 19, 2026
Our client is currently seeking a 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
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
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
- 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
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