Role Overview: 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 platform's 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.
