Senior Software Engineer, AI-Augmented Backend

WEX•Boston, MA

2d•$121,500 - $145,500•Remote

About The Position

Our backend team is in the middle of a large-scale platform modernization: incrementally retiring a legacy SOAP/Informix monolith in favor of a REST-based, event-driven architecture on PostgreSQL and Redis, applied through the Strangler pattern so that customer-facing services keep running without interruption. We're looking for a Senior Software Engineer, AI-Augmented Backend who brings deep, hands-on technical depth to this effort. This is an individual-contributor role built for someone who wants to own hard problems end to end — designing the systems, writing the critical code, and raising the bar for engineering quality across the team. If you enjoy untangling legacy systems, designing clean APIs and event pipelines, and caring about correctness in a payments environment where mistakes have real financial and compliance consequences, this role is for you.

Requirements

7+ years of professional software engineering experience, with a strong track record of building and operating backend services in production.
Deep expertise in Java and the Spring / Spring Boot ecosystem.
Strong command of relational databases — data modeling, query optimization, indexing, and transaction management.
Demonstrated experience designing and evolving RESTful APIs (versioning, error semantics, idempotency, backward compatibility).
Hands-on experience with distributed and/or event-driven systems (e.g., Kafka or similar messaging/streaming platforms).
Solid grounding in system design: you can reason about consistency, fault tolerance, scalability, and operational trade-offs, and communicate those trade-offs clearly.
Strong observability practice — you instrument services with metrics, distributed tracing, and structured logging using OpenTelemetry, Prometheus, and Grafana, and use that telemetry to define alerts, diagnose incidents, and understand production behavior.
Experience operating high-availability services: defining and meeting SLOs, designing for failover and graceful degradation, and supporting production systems through on-call.
Comfort with spec-driven development — designing against explicit contracts (OpenAPI, schemas, well-defined error formats) before writing code.
Effective, judicious use of AI-assisted development tools (spec driven development, skills), with clear ownership of the correctness, quality, and security of the resulting code
A demonstrated security mindset — you think about data protection, safe credential handling, and injection / input-validation risks by default.
Strong written and verbal communication; able to influence technical direction across a team without formal authority.
Working knowledge of Domain-Driven Design (aggregates, bounded contexts, application services).
Experience with AWS and Kubernetes (EKS), HashiCorp Vault, and Redis-compatible caching at scale.
Must be authorized to work in the United States without sponsorship.

Nice To Haves

Experience modernizing legacy systems, particularly with the Strangler pattern or comparable incremental-migration approaches.
Familiarity with Change Data Capture (e.g., Debezium) and schema-registry-backed event pipelines (Avro/Protobuf).
Experience in payments, fintech, or other regulated, high-compliance domains (PCI DSS exposure a plus).
Experience with legacy migrations off Informix or comparable enterprise RDBMS platforms.
Performance / load testing experience (e.g., k6) and capacity planning.
Proven track record with AI-driven development platforms such as Claude Code, Cursor, Augment, and GitHub Copilot.

Responsibilities

Own the design and implementation of complex backend services in Java and Spring Boot, from architecture through production rollout and operability.
Drive the incremental migration of legacy functionality toward REST using the Strangler pattern.
Design event-driven data flows (CDC pipelines, schema evolution, idempotent consumers) that keep distributed systems consistent under load.
Make and document consequential architecture decisions: API contracts, data models, consistency and transactional boundaries, failure modes, and rollback strategy.
Practice spec-driven development: define clear, machine-readable contracts and specifications (e.g., OpenAPI, schema definitions, RFC 9457 problem formats) before implementation, so that designs are unambiguous, reviewable, and consumable by both humans and AI agents.
Use AI-assisted development tooling effectively and responsibly — leveraging coding assistants and agentic workflows to accelerate delivery while owning correctness, review, and the security implications of generated code.
Own service availability and reliability: design for high availability, define and defend SLOs/error budgets, plan for graceful degradation and failover, and participate in on-call to keep critical payment paths healthy.
Build observability in from the start, not as an afterthought — instrument services with metrics, distributed tracing, and structured logging (OpenTelemetry / Micrometer), and use that telemetry to drive alerting, SLOs, and incident diagnosis.
Set the standard for code quality, testing, and operational readiness, and raise that standard through code review, design review, and technical mentorship of other engineers.
Build for a payments environment: handle sensitive cardholder data responsibly, design with PCI DSS requirements in mind, and bake in audit logging and least-privilege access from the start.
Diagnose and resolve performance and reliability issues across the stack — query optimization, caching strategy, indexing, connection management, and latency under real production traffic.
Partner with product, platform, and other engineering teams to break large initiatives into safe, shippable increments.