Join us as an AI/ML Engineer to drive core AI initiatives, develop custom LLM models, and optimize our AI-driven solutions. This role is crucial in enhancing our platforms predictive analytics and decision-making capabilities.
Responsibilities:
- Model Development and Fine-Tuning: Leverage and fine-tune LLM models.
- AI Experiments and Pipeline Setup: Design and manage robust data pipelines for efficient training and deployment of models. Conduct AI experiments to explore new technologies and methodologies.
- Operational Deployment: Assist in deploying models based on specific customer needs and contribute to the monitoring and improvement of deployed model performance.
- Innovation and Research: Stay updated with the latest AI trends, especially in tools like LangChain, Huggingface Transformers, and Weights Biases. Implement new techniques to enhance model effectiveness beyond traditional methods.
- Collaboration and System Development: Engage in architectural discussions and system design. Develop well-crafted programs and infrastructure across data handling, model deployment, and system integration.
Requirements:
- Experience: 2+ years in AI/ML with a background in building and deploying related technologies using Python/NodeJS. At least 1 year of backend engineering experience.
- Technical Skills: Proficiency with ML frameworks (PyTorch, etc.), vector databases, and techniques such as RAG. Experience in prompt design, fine-tuning LLMs, and deploying them in production environments.
- Database and Backend Skills: Experience with relational databases like PostgreSQL/MySQL and backend frameworks like NodeJS/NestJS/Django.
- Additional Skills: Understanding of rate limiting, scalability, security basics, and AWS SageMaker or equivalent services.
Preferred Qualifications:
- Solid understanding of deep learning, NLP, and the capacity to apply these skills practically. Passion for staying ahead in AI technology and applying it to solve complex problems in finance.
Benefits Perks:
- Competitive salary and equity package.
- Comprehensive health benefits.
- Flexible paid time off and supportive work policies.
- Professional development and continuous learning opportunities.
