The opportunity
We are seeking a highly motivated and technically strong professional to support the intersection of Compliance, Legal, and AI-driven technology innovation. This role will work closely with the **Legal team, MLRO, Compliance, and cross-functional stakeholders** to enhance…
What you'll do
Work closely with Legal, Compliance, and the MLRO to support internal: compliance policy reviews, files delivery, remediation tracking, and technical support.
Track compliance-related requirements for new product and service launches,: and coordinate action items with relevant stakeholders.
Partner with Legal and Compliance teams to identify, design, and implement: AI-driven solutions that improve the efficiency and scalability of compliance and legal operations.
Used the enhance intelligent systems leveraging LLMs, RAG, agentic workflows,: long-term memory, tool use, subagents, and multi-agent architectures for handling law files.
Develop benchmark frameworks and evaluation methodologies for legal and: compliance AI systems, including benchmark dataset construction, annotation strategy design, and performance measurement.
Continuously evaluate and improve systems across retrieval quality, latency,: groundedness, factuality, and task success metrics.
What they're looking for
- years of relevant experience in compliance technology, legal technology,: regulatory technology, or software/application development supporting compliance or legal functions.
- Strong understanding of relevant compliance rules, regulations, and day-to-day compliance operations.
- Bachelor’s degree required; background in AI, Computer Science, Engineering,: Law, or related disciplines preferred.
- Hands-on experience building production RAG systems, including embeddings,: vector databases, hybrid search, reranking, chunking strategies, text preprocessing, and multimodal parsing.
- Experience implementing advanced Agentic RAG approaches such as Self-RAG,: Corrective RAG, adaptive retrieval, and multi-hop reasoning workflows.
- Strong understanding of LLM and agent fundamentals, including prompt: engineering, context engineering, memory, planning, tool use, MCP, and multi-agent systems.
- Demonstrated ability to conduct independent research, solve ambiguous: problems, and rapidly prototype practical solutions.
- Strong written and verbal communication skills.
