This FinTech's AI/ML team is developing cutting edge solutions to establish a unique competitive edge for the firm. As a senior AI/ML - NLP Engineer on our team, you will be responsible for designing, developing, and implementing AI/ML models for natural language processing (NLP) applications. This would involve working with large datasets, selecting appropriate algorithms and techniques, training or fine-tuning models to achieve optimal performance, and deploying and monitoring model performance in production. You will be working in a collaborative team environment across product management, data engineering, and software engineering teams. If you are passionate about leveraging machine learning techniques to drive innovation and have a strong background in developing scalable solutions, we would love to hear from you.
Responsibilities
- Design, develop, train, and deploy AI/ML models to solve business problems through a full development and production cycle in the FinTech domain.
- Evaluate and compare the performance of different AI/ML algorithms and models.
- Utilize and improve Machine Learning Operations (MLOps) pipelines and procedures to ensure efficiency, scalability, and maintainability.
- Ensure the reliability, robustness, and scalability of machine learning models in production environments.
- Collaborate with cross-functional teams, including product managers and full stack engineers, to deliver scalable machine learning solutions.
- Understand business requirements, communicate with stakeholders, and mentor junior team members.
Qualifications
- 4-6+ (mid-career) years of experience as a hands-on data scientist or AI/ML engineer in AI/ML/DS fields.
- Advanced degree (Masters, PhD) in a relevant field (AI/ML/DS, mathematics, computer science, etc.).
- Solid understanding of Natural Language Processing techniques, including text classification, named entity recognition, and information extraction.
- Experience working with Large Language Models, such as GPT-4, Liama 2, and other commercial or open-source models in production environment.
- Proficiency in programming languages commonly used in NLP, such as Python, and libraries/frameworks like TensorFlow, PyTorch, or spaCy and strong understanding of software engineering principles and best practices.
- Strong knowledge of NLP techniques, including text data preprocessing (tokenization, stemming, and text normalization, etc.) and information extraction (summarization, and question answering, etc.)
- Knowledge of machine learning algorithms and statistical techniques, their limitations and implementation challenges
- Experience with cloud platforms and distributed computing environments for NLP tasks, such as AWS, Google Cloud, or Azure
- Experience with software development best practices, including source control (Git), CI/CD pipelines, testing, and documentation.
- Excellent problem-solving skills and the ability to work independently and collaboratively in a fast-paced, agile environment.
- Strong communication skills and the ability to effectively articulate technical concepts to both technical and non-technical audiences.
Nice to Haves
- Publications, conference talks, and/or patents in AI/ML/DS or related fields
- Experience with data visualization tools and techniques to effectively communicate and present findings.
- Experience with data transformation tool (such as dbt) and orchestration tool (such as Airflow).
- Portfolio of personal projects on Github, BitBucket, Google Colab, Kaggle, etc.
- Experience working in Finance or Financial Technology (FinTech). Understanding of regulatory and compliance requirements in the financial industry and their implications for machine learning applications.
