Machine Learning Engineer - Scout Global : Job Details

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This range is provided by Scout Global. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$180,000.00/yr - $200,000.00/yr

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AI / ML Engineer – Agentic Systems & LLMs

Next-Gen AI for Financial Services

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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Seniority level

• Seniority levelMid-Senior level

Employment type

• Employment typeFull-time

Job function

• Job functionScience
• IndustriesResearch Services

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