Full Stack Engineer

Role overview

Engineering and delivery

• Develop, test, and maintain back-end services, APIs, and web services.
• Implement SSO, security, and Kafka or MQ messaging integrations.
• Develop and deploy cloud-native AI/ML solutions using AWS, Azure, or GCP and technologies such as Docker and Kubernetes.
• Work in existing CI/CD pipelines; contribute to integration testing, development, and architecture discussions.
• Diagnose and resolve issues across development, test, and production, including performance and monitoring.
• Own solutions through production, monitoring, and support; document designs, data flows, changes, and support procedures.
• Remediate security vulnerabilities and support application-control assessments and data attestations.
• Build data pipelines and scripts; prepare, transform, validate, and analyze datasets for fraud, AML, finance, and reporting.
• Prepare datasets, run experiments, and integrate machine-learning model outputs into applications.
• Collaborate with technology teams, business stakeholders, contractors, and IT leadership; apply useful AI and emerging tools.

Must-have qualifications

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

Preferred experience

• Azure is preferred; Docker and Kubernetes experience is a plus.
• Audit, risk, or regulatory control familiarity is nice to have.
• Nice to have: AI agents, LLMs, transformers, tokenization, embeddings, prompt engineering, fine-tuning, RAG, and vector databases.
• Nice to have: LangChain, AutoGen, CrewAI, function-calling API design, data orchestration, and model-management pipelines.

Compensation and employment

• The posting identifies the role as full-time, regular, and salaried.
• The posting describes a total rewards program, bonuses, flexible benefits, competitive compensation, and stock where applicable; no salary cadence or amount is stated.

Location

• The posting lists an address in Vancouver, British Columbia.