Lead AI Engineer

Automate security: Build secure, reproducible agent sandboxes for remote dev & CI testing. Set up golden-path dev environments and guardrails for secrets/PII.

Embed governance controls: Build reproducibility, traceability, and quality-control systems that implement Bridgit's AI Governance Framework and Risk Appetite Statement. Implement monitoring and observability aligned to defined risk thresholds.

Standardise AI coding tools: Standardise AI coding tools, such as Claude Code, Cursor, and Codex. Help configure, harden, and maintain the best tools, integrating org-wide configurations with individual preferences. Track tool usage, reliability, and costs.

Champion a "shift-left" culture: Remain deeply hands-on while mentoring software developers and AI engineers to improve coding productivity without compromising code quality and develop their personalised AI-enabled workflows.

What we are looking for:

6+ years of experience in AI/ML Engineering, Machine Learning Infrastructure (MLOps), or Data Science, preferably within a regulated fintech or high-growth tech environment.

A strong understanding of modern AI architectures and experience designing and building agentic AI solutions, including tool use, workflow orchestration, routing, memory, and multi-agent frameworks and interoperability protocols (e.g., LangGraph, Semantic Kernel, AutoGen). Experience with open-source frameworks like Agno would be highly beneficial.

Experience with knowledge graph construction and graph-based retrieval (e.g. GraphRAG) for entity- and relationship-aware context grounding is highly advantageous.

Ability to implement evaluation frameworks for quality, grounding, task success, safety, latency, and cost.

Experience deploying AI solutions into production with focus on cost optimisation, scalability, observability, security, and governance.

Strong understanding of software engineering principles, architecture patterns, and delivery best practices.

Proven experience managing the full machine learning lifecycle, including model evaluation, monitoring for drift and bias, vector databases, and optimizing inference latency for production applications. Experience managing graph databases is highly beneficial.

Working knowledge of AI safety standards (NIST AI Risk Framework, ISO 42001).

Collaborative communicator: The ability to clearly deconstruct complex architectural risks to cross-functional teams, favouring building "paved roads" and secure defaults over acting as a gatekeeper.

Our Culture and Benefits

Bridgit values its team - they're the heart of how we build this business. Along with competitive remuneration, slick offices, and the chance to be part of an innovative, agile fintech, we also offer:

Extra Leave - Birthday leave plus an additional day of paid leave for life events, celebrations, or a mental health reset.

Two Weeks from Anywhere - Work remotely from a location of your choice for two weeks each year.

Learning and Development - All employees are encouraged and empowered to engage in professional development, including a number of learning initiatives run internally.

Social Events - We have a jam-packed social scene, with events throughout the year to bring the team together!

Ready to Make an Impact?

If you love the hard problems in financial engineering, and you want to build the infrastructure behind a company reshaping the lending industry, we'd love to chat. Apply now and let's build the future of finance together! 🚀