Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
2-4 years of experience in software engineering or development.
Self-starter mindset, comfortable with ambiguity.
Strong collaboration and communication skills with senior engineers.
Solid foundation in Python or another backend language.
Working knowledge of SQL and/or NoSQL databases, with basic API design experience.
Experience with AWS and data pipeline tools like PySpark or Airflow.
Responsibilities
Build and ship agent capabilities from design through production, owning your slice of the SDLC.
Harden proof-of-concept agent code into production-grade quality, focusing on error handling and observability.
Design test cases for non-deterministic agent behavior, distinct from traditional code testing.
Build and maintain data ingestion and pipeline work using AWS and big-data tooling.
Partner with Product and senior engineers to translate business requirements into shipped features.
Contribute to production support, including monitoring and incident response for AI-driven services.
Leverage AI and emerging technologies to improve productivity and streamline workflows.
Benefits
Hybrid work model (3 days per week in-office).
Opportunity to work with cutting-edge AI technologies.
Collaborative environment with senior engineers and product teams.
Focus on continuous improvement and responsible AI use.
Full Job Description
Position Summary:
Reporting to the Senior Director, Engineering, the Intermediate Software Engineer will be responsible for building and hardening Trulioo's Agentic AI Verification capabilities, taking agent-based verification workflows from proof-of-concept to production-grade systems. This role involves designing test cases and evaluation checks for agent behavior, contributing to the data pipelines that feed the agents, and partnering with Product and senior engineers to turn business requirements into shipped features.
This role is expected to leverage AI and emerging technologies to improve productivity, enhance decision-making, and continuously optimize how work is performed.
This is a full-time, permanent position based out of our San Diego or Vancouver office, working on a hybrid model (3 days per week in-office).
What You'll Be Doing:
Build and ship agent capabilities (e.g., Vertex AI-based Discovery Agents) from design through production, owning your slice of the SDLC, implementation, testing, and deployment.
Harden proof-of-concept agent code into production-grade quality, strengthening error handling, observability, and testing, with an eye on cost and latency.
Design test cases and evaluation checks for non-deterministic agent behavior, a distinct skill from testing traditional deterministic code.
Build and maintain data ingestion and pipeline work that feeds the agent, using AWS and big-data tooling.
Partner with Product and senior engineers to translate business requirements into shipped features.
Contribute to production support: monitoring, alerting, and incident response for an AI-driven service.
Leverage AI and emerging technologies to improve productivity, streamline workflows, and identify opportunities for continuous improvement while ensuring the responsible, secure, and compliant use of AI tools.
Use AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor) as a routine part of your development workflow, with a clear habit of reviewing and verifying generated output before it ships.
What You'll Bring:
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a closely related field.
2-4 years of experience in a software engineering or development role.
A self-starter mindset, comfortable with ambiguity and figuring things out rather than waiting for a fully specified task.
A strong collaborator who communicates clearly and works well alongside more senior engineers.
Solid foundation in Python and/or another backend language, with the ability to write clean, readable, testable code.
Working knowledge of SQL and/or NoSQL databases, and experience with basic API design (REST at minimum).
Experience with the following tools/technology: AWS, and exposure to data pipeline tools such as PySpark or Airflow.
Comfortable using AI coding assistants as a routine part of your workflow, and have built at least one project involving an LLM doing more than single-shot Q&A - calling tools/functions, chaining steps, or coordinating multiple steps toward a goal.
Nice to Have:
Exposure to orchestration or big-data tools (Apache Airflow, Dagster, Spark, Presto/Trino, Hive).
Exposure to infrastructure-as-code (Terraform, AWS CDK).
Interest or coursework in identity verification, fraud, security, or fintech.
