Full Stack Engineer

Role overview

Engineering responsibilities

• Develop, test, and maintain back-end services, APIs, and web services for business applications.
• Implement SSO, security, and messaging integrations, using Kafka or MQ as applicable.
• Work primarily with .NET and use Python for scripting and automation.
• Develop and deploy cloud-native AI/ML solutions on cloud platforms.
• Build, test, and deploy changes within existing CI/CD pipelines.
• Contribute to integration testing, development, and architecture discussions.
• Diagnose and resolve issues across development, test, and production environments, using monitoring tools.
• Own components through production, including monitoring and support.
• Document technical designs, data flows, changes, and support procedures.
• Remediate security vulnerabilities and support control assessments and data attestations.

Data and collaboration responsibilities

• Build data pipelines and scripts to extract, transform, and validate data for fraud, AML, and finance applications.
• Analyze large datasets for fraud detection, AML monitoring, reporting, and data quality issues.
• Prepare datasets, run experiments, and integrate model outputs into applications.
• Work with IT leadership, business stakeholders, contractors, and partner teams to deliver solutions.
• Apply AI and emerging tools where they improve development productivity or automation.

Required qualifications

• 3+ years of software engineering experience with one or more of .NET, Java, or Python.
• 2+ years of integration experience, including SSO, security, and Kafka or MQ messaging.
• Experience building and consuming APIs and working within existing CI/CD pipelines.
• Experience developing and deploying cloud-native applications on AWS, Azure, or GCP.
• Ability to diagnose issues across environments using monitoring tools such as Splunk or Elastic.
• Ability to document technical designs and data flows and collaborate with technical and business teams.
• Experience remediating security vulnerabilities and supporting application control assessments and data attestations.
• Proficiency with data extraction, transformation, and validation scripting, SQL, and large datasets.

Preferred qualifications

• Azure is preferred; Docker and Kubernetes experience is a plus.
• Audit, risk, or regulatory control familiarity is nice to have.
• AI agent and LLM development, including RAG and vector databases, is nice to have.
• Experience with LangChain, AutoGen, CrewAI, function calling, data orchestration, model management pipelines, and advanced Python is nice to have.

Benefits and culture

• The posting describes flexible benefits, coaching and development, challenging work, and a collaborative team.

Workplace

• The posting lists a Vancouver work address.