Forward Deployed Engineer (FDE), Financial Services- NYC

Forward Deployed Engineer (FDE), Financial Services- NYC

Building and deploying LLM-powered systems in production with evaluation loops and guardrails; hands-on AI deployment in real-world workflows.

About the Role

Lead end-to-end deployments of OpenAI models inside financial services firms, building production-grade, auditable AI systems that meet strict latency, correctness, and regulatory requirements. Work closely with customers and cross-functional teams to translate complex financial workflows into reliable, evaluable, and repeatable solutions.

Role

Lead end-to-end deployments of OpenAI models in financial services organizations, delivering production-grade AI systems with a focus on correctness, latency, explainability, control, and regulatory compliance. Partner closely with customers across investment banking, trading, risk, compliance, underwriting, research, and operations to translate complex workflows and data constraints into reliable production systems.

Key Responsibilities

• Design and ship production AI systems, owning integrations, data flows, reliability, observability, and on-call readiness.
• Lead discovery and scoping from pre-sales through post-production, framing problems, defining requirements, and delivering measurable outcomes.
• Define and enforce launch criteria for regulated environments, including controls, audit artifacts, evaluation benchmarks, and acceptance thresholds tied to risk and performance.
• Build systems in sensitive data environments with strong access controls, data lineage, explainability, and failure-mode considerations.
• Run evaluation loops measuring model and system quality against financial benchmarks (accuracy, latency, coverage, false positives) and drive iteration.
• Manage delivery across multiple workstreams, balancing scope, speed, and quality to protect production outcomes.
• Distill learnings into reference architectures, playbooks, tooling, and hardened primitives that scale across institutions.
• Surface field feedback to inform model behavior, product capabilities, and platform gaps.

Requirements

• 5+ years of software engineering, ML engineering, or technical deployment experience with customer-facing ownership in financial services or adjacent regulated industries.
• Experience owning complex AI or data-driven systems end-to-end in environments where errors have financial or regulatory consequences.
• Familiarity with financial workflows (research, trading, risk, compliance, underwriting, operations) and the incentives/controls that affect adoption.
• Experience deploying systems powered by LLMs/generative models and understanding of model evaluation, guardrails, and trust considerations.
• Strong communication skills across engineering, product, risk, compliance, and executive stakeholders.
• Systems thinking, high execution standards, and ability to operate under urgent, ambiguous, high-stakes conditions.

Location & Work Model

• Based in New York (NYC). Hybrid work model: typically 3 days in the office per week.
• Relocation assistance offered. Travel up to 50% may be required.

Success Metrics

• Production adoption, workflow efficiency, risk reduction, revenue impact, and evaluation-driven feedback loops that inform product, model, and GTM strategy.

Team Context

The Forward Deployed Engineering team partners with banks, asset managers, insurers, and private capital firms to embed model capabilities into enterprise systems and to create repeatable solution patterns, operating standards, and evaluation practices for regulated financial institutions.

Skills

System Design ML Engineering Software Engineering Production Deployment Observability Reliability Engineering On-call Readiness Evaluation and Benchmarking Risk Management Regulatory Compliance Data Governance Stakeholder Communication Customer-facing Delivery Systems Thinking Project/Delivery Management Architecture Operational Resilience

Experience Level

Senior

Employment Type

FullTime

• Hybrid work model (3 days in office per week)