AI/ML Engineer - Technogen : Job Details

Candidates must me local to the New York or New Jersey area and commute into the city three times a week in Midtown, NYC. ** PLEASE Only send me candidates in the NY/NJ area. **We need A senior (8+ years) AI/ML engineer with experience as a hands-on data scientist or AI/ML engineer in AI/ML/DS fields. Candidates must Design, develop, train, and deploy AI/ML models to solve business problems through a full development and production cycle in the FinTech domain as well as Evaluate and compare the performance of different AI/ML algorithms and models. Strong knowledge of NLP techniques, including text data preprocessing (tokenization, stemming, and text normalization, etc.) and information extraction (summarization, and question answering, etc.) Candidates need to me in the office 3-4 times a week (42nd St) and start onsite day one. No relocation Interview Process: Video Location: Hybrid NYC/Midtown - No Relocation - Candidates must be onsite day one and go into the office three times a week. Please provide all the below details with each submittal. It is required for the vendor Management system. Number of years working with: Total IT experience: Years working with: AI/ML Engineer Years working with: Design, develop, train, and deploy AI/ML models Years working with: NLP Years working with: CI/CD Pipelines Full Name:

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• Availability to Interview: One Day's notice
• Availability to Start: Two weeks
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• 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

• 8+ 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.