AI/ML, NLP Engineer - Assistant Vice President - Phaxis LLC
New York, NY 10165
About the Job
AI/Client, NLP Engineer – Assistant Vice President
My client a PRE IPO Fintech firm offering spectacular benefits, rated Forbes top place to work 5 years in a row is growing leaps and bounds. These are growth roles due to expansion.
Job Description
AI/Client team is developing cutting edge solutions to establish a unique competitive edge for the firm. As a Machine Learning Engineer on our team, you will play a key role in engineering the core machine learning and AI products. 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
- Build and integrate AI/Client/DS tools and workflows to address business needs and increase business efficiency.
- Support the design, development, training, and deployment of AI/Client models and engineering solutions to solve business problems through a full development and production cycle in the FinTech domain.
- Build and maintain RESTful APIs using Python and FastAPI.
- Conduct thorough project scoping sessions to understand stakeholder needs and project requirements.
- Contribute to the improvement of 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.
- For AVP level, provide technical leadership to motivate and guide team members and mentor junior engineers. For VP level, serve as the technical lead and be able to resolve technical issues during and post implementation.
- Stay updated with the latest industry trends, technologies, and best practices in machine learning and Generative AI fields.
- 5+ years of experience as a hands-on AI/Client engineer in AI/Client/DS fields for AVP level, and 8+ years of experience for VP level.
- Advanced degree (Masters, PhD) in a relevant field (AI/Client/DS, mathematics, computer science, etc.).
- Experience building, training, and deploying Client & AI models and systems in a production environment in at least one of the following applications:
- Generative AI/Large Language Model (LLM)
- Natural Language Processing (NLP)
- Experience with RESTful API development and integration, with a preference for Python and FastAPI
- Experience building APIs and infrastructure for large scale machine learning applications using AWS
- Experience working with Large Language Models, such as GPT-4, Llama 3, and other commercial or open-source models in a production environment.
- Knowledge of NLP techniques, including text data preprocessing (tokenization, stemming, and text normalization, etc.) and information extraction (summarization, and question answering, etc.)
- Proficiency in programming languages in Python, and libraries/frameworks like TensorFlow, PyTorch, spaCy and scikit-learn, etc.
- Experience with software development best practices, including source control (Git), CI/CD pipelines, testing, and documentation.
- Familiarity with database integration principles and practices, including SQL and NoSQL databases and data warehouse solutions (such as Snowflake).
- Strong problem-solving skills and the ability to work independently and collaboratively in a fast-paced, agile environment.
- Good communication skills and the ability to effectively articulate technical concepts to both technical and non-technical audiences.
- (VP Level Only) Proven track record of leading technical teams and managing complex integration projects
- Knowledge of machine learning algorithms and statistical techniques, their limitations and implementation challenges.
- 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.
- Basic knowledge of finance and business (e.g., capital markets, alternative investments)
- 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.
Source : Phaxis LLC
