Who you are

- 8+ years of software development experience

- 2+ years of technical leadership experience as a tech lead, staff engineer, engineering manager, or similar role

- Proven ability to lead technical outcomes across a team, including work you did not personally implement

- Deep production experience with Java, along with working proficiency in Python and Shell scripting

- Experience designing, building, shipping, and operating distributed real-time systems at scale

- Robust knowledge of computer systems, relational databases, and SQL

- Experience building and optimizing multithreaded and concurrent applications

- Hands-on experience with Cassandra, Yugabyte, Flink, Spark, or Kafka

- Experience with the Spring Framework

- Demonstrated use of AI coding tools such as Claude Code, Cursor, GitHub Copilot, or similar tools in real production work

- Ability to set team-level standards for AI-assisted engineering, including how tools are used, how outputs are verified, and when AI-generated suggestions should be rejected

- Strong verification discipline, with the ability to validate model outputs against source code, logs, documentation, and production behavior

- Bachelor’s degree in Computer Science or a related field is required

- Experience in fraud, risk, payments, financial services, or another domain where false negatives carry significant business or customer impact

- Experience owning ML platforms or large-scale training pipelines

- Experience with Kubernetes
- Experience building with LLM APIs, agent frameworks, tool calling, RAG, or MCP





- Experience writing evaluations or regression tests for non-deterministic systems

- Experience hiring, managing, or mentoring engineers

- Experience with test-driven development

What the job involves

- As part of the Platform Engineering team, you will help build DataVisor’s next-generation machine learning platform, combining our proprietary unsupervised machine learning technology with supervised machine learning algorithms to power real-time fraud detection at scale

- As fraud attacks become increasingly sophisticated, real-time detection is more critical than ever

- Our platform team is responsible for designing and developing the architecture that makes scalable, real-time detection possible, including streaming systems, storage layers, and training pipelines that support our core detection capabilities

- We are looking for a creative, hands-on engineering leader to help expand our streaming and database systems, improve our core detection algorithms, and automate the end-to-end training process

- This role is ideal for someone who enjoys solving complex distributed systems problems, mentoring engineers, and driving technical execution in a high-impact environment

- Join us as we continue to push the boundaries of fraud detection,



machine learning infrastructure, and large-scale data processing

- Own the technical direction of the real-time detection platform, including streaming, storage, and the training pipelines that support it

- Translate product and engineering roadmaps into clear technical plans, milestones, and execution priorities

- Proactively identify technical risks, dependencies, and trade-offs before they impact delivery

- Lead design and architecture reviews, make technical trade-off decisions, and document the reasoning behind key decisions

- Stay hands-on by coding, reviewing code, debugging issues, and supporting the team during production incidents

- Mentor engineers and raise the bar for system design, code quality, operational excellence, and technical execution

- Own the operational health of the platform, including alert quality, on-call load, incident follow-through, and root-cause prevention

- Partner directly with Product, TAM, and customer-facing teams on customer-impacting issues, ensuring clear impact assessment, prioritization, ownership, and next steps

- Define how the team uses AI agents and AI-assisted tools in engineering workflows, including verification standards and safe usage practices

- Build, evaluate, and improve LLM- and agent-assisted tools for engineering and operations use cases, such as triage, root-cause analysis, alert summarization, and evaluation harnesses

Benefits

- Stock Options

- Commuter Passes

- Paid Time Off

- Catered Lunch and Dinner

- Team and Company Outings

📌 Tech Lead of Software Engineering (Toronto)
🏢 DataVisor
📍 Toronto