Principal / Staff Engineer - Compliance

• Formulate, select, and develop technical schemes for platform projects across the compliance domain — KYC onboarding flows, identity verification and refresh, sanctions / PEP / adverse-media screening, transaction monitoring, travel-rule, case management, and regulatory reporting — including AI/ML-powered features such as intelligent screening disambiguation, document verification, anomaly detection, automated risk scoring, and analyst-assist agents.
• Organize and coordinate resources, leading project research and development, troubleshoot problems, and ensure quality and timely completion — including delivery of AI model integration work within KYC, screening, and monitoring workflows.
• Facilitate cross-team communication and promote teamwork efficiency through code quality control and collaboration, including partnering with Data Science, Risk, and Legal to productionize models and ensure reliable, auditable inference infrastructure.
• Define where probabilistic AI is appropriate versus where deterministic logic is mandatory — and design the boundary cleanly so models assist without compromising auditability or enforcement integrity.
• Undertake team tasks and cultivate technical talents in the industry, with a focus on building AI engineering best practices across the compliance engineering team — patterns for agentic coding, prompt libraries, eval suites, and human-in-the-loop review of AI-generated work in a regulated environment.

• A compliance-first systems mindset — you instinctively reach for state machines, idempotency keys, exactly-once semantics, reconciliation jobs and immutable audit logs. You understand that in compliance, false negatives cost millions and false positives cost trust, and you design accordingly. Prior experience in KYC, AML, sanctions, fraud, or payments is a strong plus; experience surviving a regulatory audit is an even stronger plus.
• Daily, fluent use of modern AI coding tools — Claude Code, Cursor, GitHub Copilot, Codex CLI, or equivalents. You can describe the specific workflows you run, the failure modes you have hit, and the guardrails you have built around AI-generated code. AI fluency is required, not nice-to-have.
• Production experience integrating AI/ML model inference into Java services — REST/gRPC-based model serving, feature engineering pipelines, latency-sensitive inference optimization, graceful degradation, and drift / rollback playbooks.
• Hands-on experience with LLM application patterns — at least one of: RAG pipelines (vector store + retrieval + grounding) for policy / KYC / case lookup, agentic workflows (tool use, multi-step reasoning, validation loops) for analyst assistance, or LLM-based classification / extraction shipped to production.
• Discipline around AI output — you do not ship LLM features without evals, hallucination tests, prompt regression suites, and human-in-the-loop review for high-stakes decisions. You have an opinion on when to reach for a frontier model vs. a fine-tuned small model vs. a deterministic rule, and can defend that trade-off to a regulator.
• Exposure to LLM integration or AI agent frameworks is a strong plus — e.g. prompt engineering at the system level, RAG pipelines, or orchestrating AI workflows within a regulated environment.
• Good understanding of software engineering basics, distributed system principles, including CAP, consistency, idempotency, and exactly-once vs at-least-once semantics.
• Clear logic, quick thinking, and good communication skills — including the ability to write clearly for non-engineers (compliance officers, auditors, regulators).

AI Proficiency Expectations & Interview Process

• AI-augmented workflow demo — walk us through how you use AI tools day-to-day. The prompts, the tasks you delegate to an agent vs. write yourself, how you review AI-generated diffs, and how you measure the impact (commit velocity, defect rate, test coverage). Concrete examples beat slogans.
• Live agentic exercise — a working session using the AI coding agent of your choice on a realistic compliance-flavoured problem. We look at how you scope, prompt, validate, and integrate AI output, not whether the agent gets it right first try.
• Design discussion with a probabilistic twist — expect at least one system-design question where the right answer involves deciding when not to use an LLM, and how to design the deterministic fallback. We hire engineers who can defend that boundary.
• Evaluation & guardrails — be ready to discuss an eval suite or guardrail you have built for an AI feature in production (e.g. an LLM-assisted SAR narrative drafting tool, an AI-assisted KYC document review step, or screening-hit disambiguation). If you have not shipped one, tell us how you would design it.

• Competitive total compensation package
• L&D programs and education subsidy for employees' growth and development — including budget for AI tools, courses, and conferences
• Various team building programs and company events
• Wellness and meal allowances
• Comprehensive healthcare schemes for employees and dependants
• More that we love to tell you along the process!