Read authentic reviews from candidates, clients and employees.
Learn more about how Great Recruiters is transforming the industry.
 

James Shen

4.90
from 82 reviews
Job
AI/ML NLP Engineer - Hybrid:
New York, New York, United States
DIRECT HIRE
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, Llama 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. 

This Job has been closed