Forward Deployed Engineer AI & Data, Financial Services

Forward Deployed Engineer – AI & Data, Financial Services

Candidates should be willing to work from client office in New York / New Jersey / SFO / Minneapolis / San Jose / Mountain View, CA (Candidates should be one of these locations / be willing to relocate).

Role Overview

We are seeking a highly experienced Forward Deployed Engineer (FDE) to design, build, and deploy AI, Generative AI, Agentic AI, and data-driven solutions for Financial Services clients. This is a hands-on, client-facing role combining deep engineering expertise, solutioning and consulting experience, and an understanding of regulated financial environments. The FDE will translate complex business problems into secure, scalable, governed, production-ready solutions.

Key Responsibilities

• Design and deploy GenAI, Agentic AI, LLM, RAG, copilots, intelligent workflows, and AI-enabled automation.
• Build production-quality solutions using Python, Java, SQL, APIs, cloud-native technologies, and modern AI frameworks.
• Demonstrate hands-on proficiency with AI-native engineering tools such as Claude Code, Devin, GitHub Copilot, Codex, Cursor, or equivalent platforms, using them to accelerate software engineering, testing, modernization, debugging, and delivery.
• Integrate AI models and agents with enterprise applications, APIs, data platforms, knowledge sources, and existing banking systems.
• Lead solutions from discovery and rapid prototyping through engineering, testing, deployment, and production transition.
• Partner directly with business, engineering, architecture, data, security, risk, and compliance stakeholders to convert ambiguous problems into implementable solutions.
• Design for security, privacy, access control, human oversight, data lineage, observability, traceability, auditability, and responsible AI.
• Create reusable engineering patterns and accelerators and coach client teams on AI-enabled engineering practices.

Required Qualifications

• 15+ years of overall experience across software engineering, data engineering, AI/ML, enterprise technology, or related areas.
• 10+ years of consulting, advisory, solution engineering, architecture, or client-facing technology delivery experience, preferably with large enterprise clients.
• Strong hands-on engineering experience with Python/Java, SQL, APIs, distributed systems, data platforms, and Azure, AWS, or Google Cloud Platform.
• Practical experience designing and delivering GenAI/LLM applications, RAG solutions, AI agents, prompt engineering, tool/function calling, orchestration, evaluation, and model integration.
• Demonstrated proficiency with Claude Code, Devin, GitHub Copilot, Codex, Cursor, or similar AI-assisted/agentic development tools, with the ability to apply them across the SDLC.
• Proven ability to take complex solutions from POC → pilot → secure, scalable production deployment.
• Strong executive and technical communication skills, with the ability to operate effectively across business and engineering stakeholders.

Financial Services Experience

• Significant experience delivering technology, data, AI, or transformation programs across Banking, Payments, Lending, Fraud/Financial Crime, Capital Markets, Wealth/Asset Management, or Insurance.
• Strong understanding of Financial Services processes and data, including customer/account data, transactions, payments, credit, KYC/AML, servicing, risk, and regulatory reporting.
• Familiarity with regulated environments and frameworks such as GLBA, PCI DSS, SOX, GDPR, FFIEC guidance, model risk management, data governance, and Responsible AI.

Preferred Qualifications

• Master’s degree in computer science, Engineering, AI/ML, Data Science, or a related discipline.
• Experience delivering Agentic AI solutions within regulated enterprise environments.
• Experience with agent orchestration, MCP/tool integration, guardrails, model gateways, evaluation, observability, human-in-the-loop controls, and AI governance.
• Experience integrating modern AI capabilities with core banking, legacy applications, enterprise data platforms, and workflow systems.
• Strong ownership, entrepreneurial mindset, and ability to operate effectively within fast-moving, ambiguous client environments.