PEAK Legal Counsel
Senior AI Engineer | Financial Institutes
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About the Role
We’re looking for a top-tier Financial Applied AI Engineer with deep, hands-on expertise in production-grade GenAI implementation for asset management and institutional finance workflows.
This is not a research role—no foundational model training, no academic experiments. We hire builders: practitioners who design, deploy, and continuously optimize real-world financial AI systems used by portfolio teams, investment research, and institutional operations. This is the high-impact, business-critical AI role that financial markets are actively racing to fill.
Core Responsibilities (Aligned with European Institutional AI Standards)
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Financial RAG & Internal Knowledge Ecosystems Design and deploy production-grade financial RAG pipelines and enterprise AI Q&A knowledge bases. Integrate diverse unstructured data—research reports, regulatory filings, meeting transcripts, market announcements, and operational documents—while systematically mitigating hallucinations for professional financial inquiry use cases.
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End-to-End AI Data Integration Connect structured alternative datasets, trading data, and asset management workflows into GenAI pipelines. Build reliable, repeatable data ingestion processes that ensure accuracy, traceability, and full compliance for institutional use.
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AI-Driven Workflow Optimisation Proactively observe day-to-day asset management operations to identify bottlenecks, manual inefficiencies, and repetitive research tasks. Design and deploy Agentic AI workflows that streamline institutional processes and boost research productivity.
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Production-Grade GenAI Delivery & Governance Transition GenAI proofs-of-concept into stable, production-ready financial environments. Implement robust evaluation, risk controls, hallucination mitigation, and compliance guardrails—ensuring AI tools are reliable, audit-ready, and adopted with confidence by front-office teams.
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Business–Technology Bridge (Core Differentiator) Act as the key translator between business and engineering. Convert institutional investment requirements into technical AI solutions, and clearly communicate model capabilities, limitations, and optimisation trade-offs back to non-technical stakeholders.
Must-Have Qualifications (Strict Expert Bar)
- Proven, hands-on experience building production financial RAG systems, institutional knowledge bases, or finance Agent workflows—portfolio projects and demos do not count.
- Deep familiarity with asset management or institutional investment daily workflows, with the ability to independently identify business pain points and translate them into AI improvements.
- Strong command of Python, LLM application engineering, prompt engineering, vector databases, and financial data processing.
- Solid experience in financial GenAI risk control, hallucination reduction, and regulated AI deployment.
- 70% engineering excellence + 30% financial business acumen—you deliver stable, usable, value-driven AI products.
- No dependency on foundational model pre-training—you excel at secondary development and industrial deployment using mature LLMs (GPT, Claude, or open-source models).
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Nice-to-Have
- Prior experience at hedge funds, asset managers, or financial FinTech institutions.
- Background in alternative financial data processing, investment research AI tools, or institutional workflow automation.
- Familiarity with financial regulatory compliance requirements for AI systems.
Why This Role
This is one of the most in-demand financial AI roles in London, Singapore, and Amsterdam:
- It goes far beyond basic content generation or simple risk controls.
- It focuses on Agentic workflow reinvention + institutional knowledge digitisation + front-office investment efficiency.
- High business impact, premium compensation, stable institutional demand, and a critically scarce talent pool.
Keywords
Financial AI · Applied AI Engineer · GenAI Finance · Asset Management AI · Financial RAG · LLM Applications · Agentic AI · Financial Data Engineering · Institutional AI · Fintech AI
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