About RavenPack

RavenPack is the leading big data analytics provider for financial services. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured content. RavenPack’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. Our clients include the most successful hedge funds, banks, and asset managers in the world!

Job Description

Our technology empowers asset managers, financial services firms, and corporate data science teams to build powerful agentic workflows on top of unstructured and structured data.

You will own end-to-end execution of projects with strategic customers, working directly with customers and internal engineering teams to implement solutions. You'll have close feedback loops with users and our Product team. In the past few months, we have:

• Rolled out the largest-scale, real-time, semantic index for Financial Documents & News

• Rolled out agent workflows for financial domain research and reporting functions

• Fine-tuned foundation models for financial domain embeddings, retrieval and search

• Rolled out an API/SDK framework for anyone to build solutions on top of our infrastructure

This role is based out of our New York City office in Soho

Responsibilities

What a day can look like:

• Meeting with executives to understand their AI strategy and workflows

• Working on a custom agent workflow for a client

• Implementing solutions to interface with client APIs

• Working with the ML team on a thorny retrieval issue

• Writing a custom document ingestion pipeline for a new class of documents

• Helping scope tradeoffs and timelines with our engineering & product teams

Your Responsibilities:

• Act as the primary technical advisor for prospective customers evaluating our systems. Demonstrate how RavenPack can address the customer's use cases through proofs of concept. Provide technical guidance on integration, deployment, and adoption best practices.

• Be the connective tissue between Data Science, Product, and Engineering, especially when it comes to frontier solutions that enable unique customer use cases.

• You have the ability to rapidly prototype solutions with constant user feedback loops. Demonstrate > Ideate.

• Guide and mentor other Solution Engineers on the team, ensuring adherence to best practices and efficient development.

• Establish processes and standards for technical design, implementation, and documentation.

• Lead technical discussions with clients, clearly articulating complex concepts and solutions.

• Collaborate with the Solutions Architect and sales teams to drive successful project execution

Qualifications

• MS in Computer Science or related field preferred.

• Experience building solutions on top of LLM APIs is a requirement

• 5+ years of experience in solutions engineering or deployment at a venture based startup or top industry AI group

• Strong Python proficiency and deep expertise in either full-stack development, search technologies, ML, Infrastructure.

• Demonstrated leadership ability, ideally with experience mentoring technical teams.

• Excellent communication – explaining complex technical solutions to both technical stakeholders and senior executives.

• Prior experience building solutions with financial domain data (structure or unstructured) is preferred.

What's in it for you?

• You will work with the latest vector search technologies, financial domain LLMs, ML models and our world-class data science team.

• Following the onboarding period, we offer a hybrid work model, allowing for up to 2 days remote per week.

• You will have ownership of projects working in a collaborative environment where we will value your contribution.

• You will work in an agile environment able to react quickly to changes with a fairly flat hierarchy.

• As we encourage continuous learning, we will support your ongoing training.

• Diversity is in our DNA! You will work in an international environment (over 29 nationalities and 24 languages spoken!)