This 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 4) Investment Management. We are 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.
Responsibilities
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.
Qualifications
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.
