Senior Software Developer, Distributed Systems & Data Platform

Caseware is one of Canada's original Fintech companies, having led the global audit and accounting software industry for over 30 years, with more than 500,000 users across 130 countries and available in 16 different languages. While you might not have heard of us (yet) over 36,000 accounting and audit professionals list Caseware as a skill on their LinkedIn profiles! At Caseware, we are evolving our cloud platform to deliver intelligent, data-driven experiences that help our customers work smarter, faster, and with greater confidence. As we expand Caseware Cloud with advanced data analytics and AI-powered agentic capabilities, we are investing in a modern, scalable data platform that serves as the foundation for insight, automation, and trust.  As a Senior Software Developer - AI Data Engineer, you will play a key role in designing and operating the data infrastructure that powers Caseware’s AI systems. You will build and scale pipelines and services that support retrieval-augmented generation (RAG), agent memory, knowledge management, and AI observability. This includes working with vector and graph storage, embedding pipelines, agent interaction data, and feedback loops that improve system quality over time.    In this role, you will take ownership of complex, production-grade AI data workflows end-to-end, influence architectural direction through technical leadership and proof-of-concepts, and help ensure our AI platform is scalable, reliable, and measurable. You will collaborate closely with AI, platform, DevOps, and product teams to turn emerging AI patterns into durable platform capabilities that directly impact customers across Caseware’s cloud ecosystem.  📍 Location: This is a fully remote position located in Colombia.  Contact Maira Russo - Senior Talent Acquisition Partner   What you will be doing:

• Design and implement reliable, scalable data ingestion and integration pipelines for structured, semi-structured, unstructured, and multi-modal data (e.g., databases, documents, APIs, events), ensuring data is AI-ready, governed, secure, and observable. 

• Build and scale retrieval infrastructure, including vector storage, embedding pipelines, hybrid search, and graph-based knowledge representations, while optimizing data modeling for retrieval quality. 

• Develop and operate agent memory systems and pipelines for AI system signals (tracing, feedback, evaluation, and usage data) to support observability and continuous improvement. 

• Apply data quality, validation, monitoring, and testing frameworks in production pipelines, ensuring governance, access control, lineage, and security standards are met, including safe handling of sensitive data in AI retrieval and generation workflows. 

• Monitor, troubleshoot, and optimize AI data pipelines and retrieval workflows for reliability, performance, and cost, with strong observability and resilient processing patterns. 

• Design and support evaluation workflows for AI systems, enabling offline testing, benchmarking, and continuous improvement of retrieval and agent performance over time. 

• Lead pragmatic platform evolution by defining clear contracts between AI services and data systems, reducing coupling, and improving developer experience. 

What you’ll bring:

• Strong software engineering fundamentals, including designing maintainable, testable systems and owning features end-to-end. 

• Production experience with distributed systems, including async workflows, failure modes, retries, and eventual consistency. 

• Hands-on experience building and operating data pipelines for AI systems, such as embeddings pipelines, retrieval workflows, or feedback data processing. 

• Experience working with AI-related data infrastructure, including vector databases, search systems, or graph-based storage. 

• Experience with retrieval systems (RAG), embedding pipelines, or hybrid search (vector + keyword). 

• Experience with agent frameworks, agent memory systems, or orchestration of tool-using AI systems. 

• Experience designing pipelines for observability data, including traces, logs, metrics, or user feedback loops for AI evaluation. 

• Experience operating production systems, including monitoring, incident response, and continuous improvement. 

• Cloud experience on AWS building production systems, including storage, messaging, and orchestration. 

• Experience with infrastructure as code, with CDK preferred and CloudFormation or Terraform acceptable. 

• Strong collaboration and communication skills, with the ability to mentor and raise engineering maturity through reviews and design discussions. 

• Strong English language communication and collaboration skills. 

Strongly preferred (high-signal) The Tech Stack You’ll Work With:

• Backend & Platform: TypeScript, NestJS, Python 

• Cloud & Infrastructure: AWS EKS, AWS Lambda, AWS Bedrock, AWS AgentCore 

• Search & Retrieval: AWS OpenSearch Serverless, AWS S3 Vectors, AWS Knowledge Bases 

• Document & Data Processing: AWS Textract, DynamoDB, S3 

• AI Evaluation & Observability: LangFuse, LangSmith (or equivalent) 

• AI-assisted development tools: GitHub Copilot, AWS Kiro 

• Developer Tooling: GitHub, GitHub Actions, Nx Monorepo 

• Collaboration: Jira, Confluence, Microsoft Teams, Outlook 

Why this role exists What success looks like (first 6–12 months) Perks & Benefits