AI/ML NLP Engineer - Hybrid

Our direct client, a fast-growing FinTech firm in New York City, is looking for an AI/ML NLP Engineer. The 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.

2 or 3 days onsite work in the NYC midtown office is required. Base salary is in the $160-200K range DOE, plus generous bonus and stock options.
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.).
• 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.