Machine Learning Engineer - LLMs

September 4

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Connecting data scientists/engineers and companies, perfectly.

Marketplace for Hiring Data Scientists • Staffing & Recruiting • Machine Learning • Big Data • Analytics

11 - 50

Description

• Lead the end-to-end development and deployment of predictive models for wealth management solutions, from database to user interface. • Design, build, and implement AI Co-Pilots, specifically tailored for the Wealth and Asset Management industry. • Partner with the Director of Artificial Intelligence to conceptualize and execute a comprehensive strategy for integrating AI across business units. • Rapidly prototype new algorithms and models, and transition from prototype to production environment, ensuring scalability and robustness. • Develop full-stack solutions, including database schema design, back-end logic, and front-end presentation. • Measure and optimize the performance of both machine learning models and the full-stack applications, ensuring they align with business objectives. • Collaborate with cross-functional teams to ensure that AI solutions enhance user experience and add significant business value. • Act as a technical leader within the team, providing guidance and mentorship to other engineers.

Requirements

• Minimum of 4+ years of experience in machine learning and full-stack development. • Demonstrated experience building and deploying machine learning models, as well as constructing and maintaining full-stack applications. • Proven track record of building and deploying machine learning models in a business context. • Proficient in utilizing a range of machine learning libraries and frameworks (such as TensorFlow, PyTorch, Scikit-learn, Keras, etc.) to build, train, and deploy models efficiently. • Proficiency with the LangChain framework, including experience in building applications with complex LLM integrations, using retrieval-augmented generation for contextual search, and adeptness in prompt engineering for effective human-AI interaction. • Strong engineering skills, including proficiency in Python. • Familiarity with cloud platforms (AWS, GCP, Azure) and understanding of containerization and orchestration tools (Docker, Kubernetes). • Ability to rapidly prototype and innovate, while maintaining a focus on scalable solutions. • Strong problem-solving skills and the ability to learn on the job, staying ahead of the latest industry trends. • Self-starter, capable of learning on the job and adapting to new challenges.