Applied AI / Machine Learning Engineer

Location: New York, NY Metro Area (On-Site)

About the Opportunity

Join a high-velocity, centralized technology group building the foundational artificial intelligence platform for a leading financial services organization. This hands-on, code-first role focuses on designing state-of-the-art agentic workflows, custom LLM infrastructure, and high-throughput retrieval systems directly driving business outcomes. You will bridge the gap between bleeding-edge generative models and production-grade execution in a fast-paced, highly quantitative environment.

Responsibilities

• System Architecture & Deployment: Design and maintain high-efficiency AI infrastructure, including dynamic model routing, automated evaluation frameworks, and low-latency inference setups on dedicated hardware.

• Agentic Framework Development: Construct autonomous multi-agent systems to automate software development, complex document analysis, and large-scale data workflows across distributed systems.

• Enterprise Search & Retrieval: Scale search architectures leveraging advanced Retrieval-Augmented Generation (RAG) models, vector storage solutions, and tailored embedding pipelines.

• Stakeholder Collaboration: Work alongside cross-functional domain experts and analysts to convert complex operational and research challenges into production-ready, AI-driven applications.

• MLOps Implementation: Establish robust MLOps practices, including continuous deployment pipelines, model monitoring, version control, drift management, and systematic quality evaluation metrics.

Requirements (Must-Have)

• 6+ years of software engineering and machine learning experience (or 4+ years paired with an advanced quantitative degree).

• Exceptional Python programming skills paired with deep experience in deep learning frameworks such as PyTorch or JAX.

• Proven experience building production applications with advanced LLM orchestration tools, structured output architectures, and protocol-driven tool integration.

• Direct experience utilizing vector storage solutions, container technologies, and deploying applications on distributed cloud environments (AWS, Azure, or GCP).

• Strong technical focus on system optimization, compute efficiency, latency reduction, and a commitment to shipping reliable production code.

Preferred Qualifications (Nice-to-Have)

• Prior background delivering advanced AI tools or alternative data pipelines within quantitative finance, investment management, or premier technology firms.

• Proficiency in a compiled programming language (such as C++, Go, or Java).

• Solid grounding in traditional statistical machine learning methodologies (time-series analysis, clustering, regression) alongside generative models.

Compensation & Benefits

• Competitive base compensation package paired with performance-based incentive opportunities.

• Access to high-performance computational hardware and specialized proprietary data assets.

• Comprehensive medical, dental, and vision insurance coverage.

• Equal Opportunity Employer: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, or protected veteran status.