AI / ML Engineer – Agentic Systems & LLMs
Next-Gen AI for Financial Services
📍 Location: New York City
💰 Compensation: $200,000+ (Equity included)
The Role:
We’re working with a breakthrough AI platform. This team is pioneering agentic systems and LLM-driven infrastructure tailored to financial analysis at enterprise scale.
As an AI / ML Engineer, you’ll be at the heart of their agentic AI stack — designing, optimizing, and deploying multimodal retrieval and LLM systems that surpass human-level output across high-stakes use cases.
What You’ll Do:
- Build, fine-tune, and integrate large language models (LLMs) and embedding systems into high-performance AI agents.
- Architect multimodal retrieval pipelines that seamlessly combine structured and unstructured data.
- Lead development of modular, function-calling-based agentic frameworks for intelligent task execution.
- Collaborate cross-functionally with product and infrastructure teams to deliver real-world, production-grade AI features.
- Rapidly prototype and iterate on new AI tools driven by client feedback and research insights.
- Shape next-gen AI orchestration systems for enterprise use, with a focus on accuracy, auditability, and speed.
What You Bring:
- 2+ years of experience deploying machine learning models in production environments.
- Proven skill with LLM APIs (e.g., OpenAI, Claude) and experience fine-tuning open-source models.
- Deep familiarity with semantic search tools like FAISS, Weaviate, Pinecone, PGVector, or Chroma.
- Strong Python background and experience with tools like Hugging Face, PyTorch, TensorFlow, or LangChain.
- Experience developing and deploying multimodal retrieval solutions across diverse financial data types.
- Ability to independently scope, prototype, and ship AI features from scratch.
Bonus Points For:
- Experience building modular agentic systems using function calling and orchestration frameworks.
- Deep understanding of financial data modeling, retrieval, or private market data ecosystems.
- Familiarity with scaling AI pipelines using Ray, Kubernetes, or distributed computing frameworks.
- Startup experience or entrepreneurial mindset.
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