Principal Software Developer

ABC Fitness

1d•Remote

About The Position

ABC Fitness is seeking a Principal Software Developer to lead the architecture, design, and delivery of scalable, highly available distributed systems and enterprise applications. The role combines hands-on software development with technical leadership across teams, using Java, Spring Boot, microservices, cloud-native technologies, and AWS. The position drives system design, architectural standards, engineering quality, production readiness, and modernization while translating business problems into practical technical plans. The developer will also design and integrate AI-powered capabilities into applications and engineering workflows. This includes practical work with LLMs, prompt engineering, retrieval-augmented generation, AI orchestration, agentic workflows, and related production concerns such as evaluation, safety guardrails, security, observability, latency, and cost. The role provides technical direction and mentorship without relying on direct reporting authority, and involves collaboration with Product, QA, DevOps, Security, and Platform teams. ABC Fitness describes its culture as centered on respect, trust, belonging, continuous growth, and teamwork. The role's anticipated annual compensation range is CAD $136,100–$147,800; other compensation may be offered where applicable. The posting also describes an open PTO policy, quarterly company-wide days off, parental and pawternity leave, a quarterly fitness reimbursement, medical, dental, and vision coverage, an EAP, and a Calm App subscription.

Requirements

Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Typically 6–10 years of professional software engineering experience, including significant experience designing and delivering enterprise-scale applications.
Strong hands-on expertise in Java, Spring Boot, microservices, and object-oriented programming.
Proven experience designing distributed systems, scalable APIs, and high-availability architectures.
Deep understanding of system design concepts including caching, concurrency, database transactions, consistency, fault tolerance, and asynchronous processing.
Experience with AWS or comparable cloud platforms, containerization, and Kubernetes.
Experience with RESTful APIs, API gateways, authentication, authorization, and enterprise integration patterns.
Strong knowledge of relational databases, SQL optimization, and data modeling.
Experience with event-driven systems and messaging platforms such as Kafka, SQS, or equivalent technologies.
Strong understanding of CI/CD, automated testing, observability, and production troubleshooting.
Practical experience using AI-assisted development tools and applying AI capabilities to engineering problems.
Working knowledge of LLM integration, RAG, agentic workflows, or AI orchestration, supported by hands-on implementations or meaningful technical projects.
Strong problem-solving abilities and demonstrated ownership of complex technical initiatives.
Excellent communication skills and ability to influence technical decisions across teams.

Nice To Haves

Experience in billing, payments, financial transactions, collections, or commerce platforms.
Experience designing customer-facing APIs and API-first platforms.
Knowledge of payment processing, transaction integrity, idempotency, reconciliation, and PCI-related security considerations.
Experience with OpenTelemetry, Grafana, distributed tracing, and modern observability platforms.
Experience with Redis, Kafka, Kubernetes, Docker, and infrastructure-as-code.
Experience with API management platforms such as Gravitee, Apigee, or Kong.
Hands-on experience with Amazon Bedrock, LangChain, LangGraph, or similar AI orchestration technologies.
Experience designing and deploying production AI applications, including evaluation frameworks, monitoring, and guardrails.
Familiarity with MCP servers, tool integrations, and enterprise agentic systems.
Experience driving AI-assisted SDLC automation or engineering productivity initiatives.
Experience mentoring engineers and driving architectural improvements across multiple teams.
Demonstrated ability to balance technical innovation with production reliability, security, and business priorities.

Responsibilities

Lead architecture and design of scalable, highly available, fault-tolerant distributed systems using Java, Spring Boot, microservices, and AWS.
Drive end-to-end system design, including API contracts, data modeling, service boundaries, integration patterns, and deployment architecture.
Apply Domain-Driven Design, event-driven architecture, object-oriented principles, and established design patterns.
Design and optimize RESTful APIs, asynchronous messaging systems, and event-driven integrations using Kafka and other messaging technologies.
Make architectural decisions addressing scalability, consistency, performance, reliability, security, and cost optimization.
Lead technical design reviews, assess architectural trade-offs, and establish reusable engineering patterns across teams.
Identify technical debt, architectural bottlenecks, and modernization opportunities.
Remain hands-on with coding, prototyping, complex feature implementation, and critical technical problem-solving.
Develop and maintain enterprise applications using Java, Spring Boot, SQL/NoSQL databases, and cloud-native technologies.
Establish engineering standards for clean and secure code, automated testing, code reviews, and continuous integration.
Drive Test-Driven Development, automated testing, and shift-left quality practices.
Troubleshoot complex production issues, conduct root cause analysis, and implement sustainable corrective actions.
Optimize system performance, API latency, database efficiency, and resource utilization.
Promote production readiness through observability, distributed tracing, structured logging, monitoring, and proactive alerting.
Design and integrate AI-powered capabilities into enterprise applications and engineering workflows.
Evaluate and implement AI workflows, tool calling, and Model Context Protocol integrations where appropriate.
Develop proofs of concept and production-oriented solutions using AI APIs, orchestration frameworks, and enterprise integration patterns.
Address AI engineering considerations including evaluation, hallucination mitigation, guardrails, security, observability, latency, and cost.
Identify opportunities to improve engineering productivity through AI-assisted development, testing, documentation, and troubleshooting.
Collaborate with engineering teams to introduce AI-enabled solutions that deliver measurable business or engineering value.
Serve as a technical authority and mentor for senior engineers, technical leads, and developers.
Influence architectural direction and engineering practices across multiple teams without direct reporting authority.
Collaborate with Product, QA, DevOps, Security, and Platform teams to deliver end-to-end solutions.
Translate ambiguous business problems into clear technical designs and actionable engineering plans.
Drive technical discussions, resolve complex engineering challenges, and communicate architectural decisions effectively.
Evaluate emerging technologies and recommend adoption based on business value, maintainability, and long-term sustainability.