Design, develop, and deploy advanced AI/ML solutions that power the next generation of financial technology.
Implement GenAI, agent-based systems, and sophisticated ML models to enhance our platform capabilities.
Own the Full AI Lifecycle: Design and implement robust data models that support AI/ML initiatives.
Collaborate with cross-functional teams to integrate AI/ML functionalities into our multi-product, multi-issuer platform.
Develop scalable machine learning pipelines and data processing workflows.
Build, test, and optimize AI models on various cloud platforms; AWS experience (including SageMaker and Bedrock) is a bonus.
Ensure robust deployment practices and maintain the performance and scalability of AI systems.
Architect and implement an Agentic Framework tailored specifically for the needs of Financial Advisors, enabling autonomous reasoning, planning, and execution across complex financial scenarios.
Develop and enhance OCR capabilities and integrate these with vector databases.
Champion MLOps best practices to streamline the continuous integration, delivery, and deployment of machine learning models.
Provide technical guidance and mentorship to team members.

Minimum 3 years of professional experience in AI/ML engineering or a related field.
Must have hands-on experience working on a commercial product that is already in production.
In-depth knowledge of GenAI, agent-based systems, ML models, and prompting techniques.
Practical experience with OCR technologies, vector databases, and Retrieval Augmented Generation (RAG).
Proficient in programming languages such as Python and familiar with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Experience with cloud platforms is beneficial; AWS experience (specifically with AWS SageMaker and Bedrock) is a plus but not required.
Strong problem-solving abilities.
Excellent communication skills and a collaborative mindset.
Ability to thrive in a fast-paced, innovative environment.
Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or a related discipline.
Experience in the financial technology sector, particularly with structured products or annuities.
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
Experience with DevOps best practices and contributing to open-source projects.

Focal

Open Positions (1)