Applied AI Engineer - Associate Director - Capital Markets

Applied AI Engineer - Associate Director

Financial Services / Banking / Legal Consultancy - Capital Markets

📍 New York (preferred) or London - Hybrid/flexible working

· Competitive compensation and attractive benefits package

This is a hands-on role for an experienced Applied AI engineer with genuine banking, capital markets or legal domain experience, someone who wants to architect and build production AI systems directly, not just oversee them.

You’ll own delivery, quality, evaluation and partner management across the AI Solutions portfolio from prototype through to real, scaled impact for enterprise, client services and product teams.

Reporting to the Head of AI Solutions, you’ll be part of a growing team mandated to build and embed AI capability across the business.

Responsibilities:

• Take current AI initiatives from prototype / MVP to robust, scalable, production-grade delivery architecting and building the next phase of AI agents, GenAI capability and workflows that create real impact.
• Drive rollout of internal AI capabilities and agents in line with the internal AI deployment roadmap, and oversee AI-driven upgrades to priority products and solutions.
• Set direction across the AI Solutions portfolio: product requirements, agent orchestration, architecture, evaluation harnesses and delivery priorities.
• Partner closely with SMEs, Product teams and functional leads to keep solutions accurate, grounded and genuinely useful in real workflows.
• Define the practical connectivity, data and delivery models including MCP servers and packaged AI Skills that let applications and workflows plug in across environments.
• Own AI quality, guardrails and governance across the portfolio: evaluation harnesses, groundedness / traceability checks, observability and tracing, confidence frameworks, and secure / safe-use boundaries, internally and externally.
• Design AI workloads with cost front of mind, balancing performance and reliability against inference and infrastructure spend.
• Manage external partners and specialist vendors, keeping work aligned to priorities, timelines, architecture and ownership model.
• Help build durable in-house AI capability protecting IP while accelerating adoption of products and services.
• Contribute to wider AI strategy, portfolio reporting and risk visibility, communicating progress clearly and concisely to stakeholders.

Skills & Experience:

• A proven track record shipping production AI systems that serve real users from prototype through to controlled rollout, not just POCs.
• Hands-on engineering depth in Python (TypeScript/React and Java both a plus), and genuine comfort building production-grade systems yourself.
• Real expertise across the modern AI stack: LLMs, RAG (hybrid retrieval, reranking, embedding selection), knowledge graphs/ontologies, agentic workflows, and rigorous evaluation (eval harnesses, LLM-as-judge, groundedness/faithfulness metrics, tracing).
• Experience delivering secure, enterprise-grade AI governance, permissions, auditability, and private retrieval architectures that keep proprietary data out of external models.
• Hands-on experience with cloud-native AI infrastructure (e.g. AWS Bedrock, Neptune, OpenSearch, Lambda, or equivalent services on Azure/GCP) is important. Strong engineers able to ramp quickly on our stack will also be considered.”
• The ability to translate complex domain requirements into usable products for Financial markets, Capital markets, Banking, Consultancy Services Regtech, Legal or other data-intensive domains ideal, but fast learners welcome.
• Product and architecture thinking: defining use cases, shaping roadmap, and connecting technical delivery to business outcomes.
• Confidence managing external vendors and PoC work while keeping direction, quality and IP firmly in-house.
• A collaborative style able to work closely with subject matter experts, turning specialist feedback into real product improvements.
• Comfort with structured data, taxonomies and ontology-style mappings across complex schemas.
• Experience running user testing, pilots and adoption/training loops with specialist user groups.
• Sound judgment on AI risk hallucination, groundedness, security, explainability and safe deployment boundaries.
• A degree in Computer Science, AI, Data Science, or related field (and equivalent experience) is a plus.
• Excellent communicator able to influence stakeholders across the business and translate technic…