AI Forward Deployed Engineer (AI FDE)

About the Role
We are looking for an AI Forward Deployed Engineer (AI FDE) to work directly with business, investment, operations, risk, and technology teams to identify high-value opportunities for applying AI across Capital Markets, Wealth Management, and Asset Management.
This is a high-impact, hands-on role at the intersection of AI engineering, financial services, product development, and client/business problem solving. You will take ambiguous business problems, understand the underlying workflows and data, prototype AI-powered solutions rapidly, and take successful solutions into production.
The ideal candidate combines strong software engineering fundamentals with practical experience building LLM/GenAI applications, agentic workflows, data pipelines, and enterprise AI solutions. You should be comfortable working with senior stakeholders as well as writing production-quality code.
What You Will Do
1. Identify & Solve Business Problems
Work directly with portfolio managers, investment professionals, wealth managers, research teams, traders, operations, risk, compliance, and senior business stakeholders.
Understand complex financial workflows and translate business problems into AI-enabled solutions.
Identify processes where AI can deliver measurable improvements in productivity, revenue, risk management, client experience, or operational efficiency.
Rapidly prototype solutions and validate them with end users.
2. Build AI-Powered Applications
Design and build production-grade applications using LLMs, RAG, AI agents, workflow automation, and traditional ML.
Develop AI copilots and agents for use cases such as:
Investment research and market intelligence
Equity, credit and macro research
Portfolio analysis and monitoring
Wealth advisor and relationship-manager copilots
Client reporting and communication
Investment proposal generation
Financial-document intelligence
Earnings and company analysis
Regulatory and compliance workflows
Trade and post-trade operations
Risk monitoring and exception management
Knowledge management and enterprise search
Integrate AI applications with internal data, APIs, databases, market-data platforms, and enterprise systems.
3. Own Solutions End-to-End
Take solutions from problem discovery → prototype → pilot → production → adoption.
Build scalable APIs, services, data pipelines, and user interfaces where required.
Establish appropriate evaluation frameworks for LLM and agentic applications.
Monitor production systems for accuracy, latency, reliability, cost, and business impact.
Iterate rapidly based on user feedback.
4. Work With Modern AI Technology
Work with LLMs and foundation models from leading providers and/or internally hosted models.
Build applications using techniques such as:
Retrieval-Augmented Generation (RAG)
Tool/function calling
Agentic workflows
Structured outputs
Prompt engineering
Fine-tuning where appropriate
Embeddings and vector search
LLM evaluation and observability
Model routing and cost optimization
Evaluate new AI technologies and determine where they can create practical business value.
5. Financial Services Data & Domain
Work with structured and unstructured financial data including research reports, filings, presentations, market data, portfolio data, client information, transactions, and internal knowledge.
Develop solutions that account for the requirements of financial-services environments, including security, privacy, auditability, model risk, data governance, and regulatory controls.
Understand the difference between an impressive AI demo and a solution that can safely operate in a regulated financial institution.
Required Skills & Experience
3–8 years of experience in software engineering, data engineering, ML/AI engineering, quantitative technology, or a similar technical role.
Strong proficiency in Python and modern software engineering practices.
Experience building and deploying production applications using APIs, databases, cloud infrastructure, and distributed systems.
Hands-on experience with Generative AI / LLM application development.
Strong understanding of RAG, embeddings, vector databases, LLM APIs, tool calling, and agentic architectures.
Experience with at least one major cloud platform such as AWS, Azure, or GCP.
Familiarity with modern AI/ML frameworks and developer tooling.
Strong understanding of software development lifecycle, testing, Git, CI/CD, observability, and production engineering.
Ability to work with SQL and structured/unstructured data.
Strong problem-solving skills and ability to work with ambiguous requirements.
Excellent communication skills, with the ability to explain technical concepts to investment and business stakeholders.

Work arrangement
No