NLP Engineer

1 week ago

New York, New York, United States Action Tech Full time

This role is a hybrid position requiring onsite presence for four days a week.

Candidates must be US Citizens or Green Card holders and currently reside in the tri-state area.

Available positions include AVP or VP level.

Position Overview

The AI/ML division is at the forefront of creating innovative solutions that provide a distinct competitive advantage for Action Tech. As a senior AI/ML - NLP Engineer, you will play a pivotal role in the design, development, and implementation of AI/ML models tailored for natural language processing applications. This entails working with extensive datasets, selecting suitable algorithms, training or fine-tuning models for peak performance, and overseeing model deployment and monitoring in a production setting. You will collaborate closely with teams across product management, data engineering, and software engineering. If you are driven by the desire to utilize machine learning techniques to foster innovation and possess a robust background in scalable solution development, we encourage you to consider this opportunity.

Key Responsibilities

  • Design, develop, train, and deploy AI/ML models to address business challenges throughout the complete development and production lifecycle in the FinTech sector.
  • Assess and compare the effectiveness of various AI/ML algorithms and models.
  • Enhance and streamline Machine Learning Operations (MLOps) pipelines and procedures to guarantee efficiency, scalability, and maintainability.
  • Ensure the reliability, robustness, and scalability of machine learning models in live environments.
  • Work collaboratively with cross-functional teams, including product managers and full-stack engineers, to deliver scalable machine learning solutions.
  • Comprehend business requirements, engage with stakeholders, and mentor junior team members.

Qualifications

  • 4-6+ years of hands-on experience as a data scientist or AI/ML engineer in relevant fields.
  • Advanced degree (Masters, PhD) in a pertinent discipline (AI/ML/DS, mathematics, computer science, etc.).
  • Thorough understanding of Natural Language Processing techniques, including text classification, named entity recognition, and information extraction.
  • Experience with Large Language Models, such as GPT-4, Liama 2, and other commercial or open-source models in production settings.
  • Proficiency in programming languages commonly used in NLP, such as Python, along with libraries/frameworks like TensorFlow, PyTorch, or spaCy, and a solid grasp of software engineering principles and best practices.
  • Strong knowledge of NLP techniques, including text data preprocessing (tokenization, stemming, text normalization, etc.) and information extraction (summarization, question answering, etc.).
  • Familiarity with machine learning algorithms and statistical techniques, including 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 abilities and the capacity to work both independently and collaboratively in a dynamic, agile environment.
  • Strong communication skills with the ability to effectively convey technical concepts to both technical and non-technical audiences.

Preferred Qualifications:

  • Publications, conference presentations, 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 tools (such as dbt) and orchestration tools (such as Airflow).
  • A portfolio of personal projects on platforms like Github, BitBucket, Google Colab, Kaggle, etc.
  • Experience in Finance or Financial Technology (FinTech), with an understanding of regulatory and compliance requirements in the financial industry and their implications for machine learning applications.

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