Build and operationalize LLM applications for financial services. Responsibilities include developing production AI features, Python libraries, data pipelines, prompts, tool-calling workflows, retrieval systems, evaluation suites, monitoring, and responsible-AI safeguards. Diagnose hallucination, latency, and cost issues; define metrics; analyze failures; collaborate with product, engineering, and business teams; and document designs and findings.
Join Cantor Fitzgerald Technology Markets LLC as a Machine Learning Engineer focused on building AI‑driven solutions for a high‑volume financial services business. You will work closely with product, engineering, and business teams to create, test, and operationalize large language model (LLM) applications, ensuring they meet performance, reliability, and responsible‑AI standards.
Responsibilities
- Design and implement LLM‑driven features in production systems.
- Build and maintain data pipelines for both structured and unstructured data.
- Write clean, testable Python code and maintain reusable libraries.
- Develop prompts, tool‑calling workflows, and retrieval pipelines.
- Create evaluation suites, define success metrics, and analyze failures.
- Diagnose and mitigate hallucination, latency, and cost issues.
- Collaborate with product, engineering, and business stakeholders.
- Implement monitoring, logging, and alerting for AI services.
- Contribute to responsible‑AI guardrails and human‑in‑the‑loop processes.
- Document designs, experiments, and findings for internal knowledge sharing.
- Bachelor’s degree in computer science, machine learning, mathematics, physics, statistics, econometrics, or equivalent practical experience.
- Experience contributing to production or production‑like software through work, internships, research, open source, or substantial personal projects.
- Strong programming ability in Python with clear, tested, and maintainable code.
- Experience with web services, data integrations, testing, logging, and basic monitoring across diverse data types.
- Hands‑on experience building with LLM tools or frameworks (prompting, structured outputs, tool‑calling, retrieval, multi‑step workflows) and awareness of common failure modes.
- Experience evaluating LLM‑powered applications: building test sets, reviewing failures, defining metrics, and iterating on prompts or retrieval.
- Solid grounding in machine learning, statistics, and experimental design with ability to interpret technical papers and documentation.
- Strong communication skills and comfort working with product, engineering, and business partners.
- Interest in applying AI responsibly in financial services, including privacy, security, human review, and appropriate automation.
- Familiarity with cloud deployment, containers, and modern release pipelines.
$140,000 - $160,000
Cantor Fitzgerald New York, New York, USA Office
499 Park Avenue, New York, NY, United States, 10022
Similar Jobs
Fintech • Machine Learning • Payments • Software • Financial Services
Owns end-to-end endpoint detection coverage, including telemetry requirements, behavioral detection engineering, alert deployment, tuning, and coverage gap analysis. The role develops Detection-as-Code rules, applies MITRE ATT&CK and threat research, leverages AI and machine learning, collaborates with security operations and threat intelligence teams, and mentors engineers. Responsibilities include endpoint log analysis, EDR engineering, adversary emulation alignment, risk communication, compliance documentation, and high-fidelity detection development at enterprise scale.
Top Skills:
SparkCi/CdCrowdstrike FalconDatabricksDetection-As-CodeEdrGitLinuxLlmsMachine LearningmacOSMicrosoft DefenderMitre Att&CkPythonSentineloneSIEMSQLStreaming Data PlatformsSysmonWindowsWindows Event LogsYaml
Fintech • Machine Learning • Payments • Software • Financial Services
Owns ML infrastructure and model delivery product roadmaps for Capital One’s Card Intelligence team. Defines customer-focused problems, prioritizes capabilities, establishes success metrics, partners with data science and technology teams, leads agile ceremonies, and drives scalable solutions aligned with business outcomes. Requires product management experience or a quantitative, business, or marketing degree, with preferred experience in data engineering, MLOps, machine learning infrastructure, or software engineering.
Top Skills:
AgileData EngineeringGenerative AiMachine LearningMl InfrastructureMlopsSoftware Engineering
Fintech • Machine Learning • Payments • Software • Financial Services
Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization. Collaborate with engineers, research scientists, technical program managers, and product managers to deliver responsible AI-powered products and shape the long-term roadmap for foundational AI platforms.
Top Skills:
AWSAws UltraclustersAzureC#C++GoGCPHugging FaceJavaLarge Language ModelsMachine LearningNemo GuardrailsPythonPyTorchScalaSimilarity SearchVectordbs
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
