Applied AI/ML Engineer

Responsibilities

• Design and maintain high-efficiency AI infrastructure, including dynamic model routing, automated evaluation frameworks, and low-latency inference setups on dedicated hardware.
• Construct autonomous multi-agent systems to automate software development, complex document analysis, and large-scale data workflows across distributed systems.
• Scale search architectures leveraging advanced Retrieval-Augmented Generation (RAG) models, vector storage solutions, and tailored embedding pipelines.
• Work alongside cross-functional domain experts and analysts to convert complex operational and research challenges into production-ready, AI-driven applications.
• Establish robust MLOps practices, including continuous deployment pipelines, model monitoring, version control, drift management, and systematic quality evaluation metrics.

Requirements

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

Preferred Qualifications

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