Machine Learning Engineer - LLMs
Description For Machine Learning Engineer - LLMs
HopHR is seeking a Machine Learning Engineer/Senior Data Scientist to lead the creation of Motive's Financial Advisor co-pilot. This role is at the forefront of AI-driven solutions, crafting the predictive models that will underpin the next generation of wealth management products. The ideal candidate excels in machine learning and has been exposed to full-stack applications.
The position is exclusively open to candidates based in Brazil, Argentina, and Colombia. The firm was formed in 2016, born out of a vision and desire to innovate the private equity and venture capital industry and to capitalize on the significant financial technology opportunity. They are a specialist investment firm that invests in software, information & investment services companies providing mission-critical products and services across five core sub-sectors: 1) Banking & Payments 2) Capital Markets 3) Data & Analytics 4) Insurance, and 5) Investment Management.
As a Machine Learning Engineer - LLMs, you will be responsible for leading the end-to-end development and deployment of predictive models for wealth management solutions. You will design and implement AI Co-Pilots tailored for the Wealth and Asset Management industry, partner with the Director of Artificial Intelligence to integrate AI across business units, and develop full-stack solutions.
The role requires a minimum of 4+ years of experience in machine learning and full-stack development, proficiency in Python and various ML frameworks, and familiarity with cloud platforms and containerization tools. You should be a self-starter with strong problem-solving skills and the ability to adapt to new challenges.
Join HopHR and be at the forefront of AI-driven solutions in the financial sector, working remotely while contributing to innovative wealth management products.
Responsibilities For Machine Learning Engineer - LLMs
- 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 For Machine Learning Engineer - LLMs
- 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.
- Bachelor's degree in a STEM field is required
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