ML Engineer I

Key Responsibilities

• ML Model Development & Optimization: Design, develop, and implement robust machine learning models using Python and its associated libraries. You will be responsible for the end-to-end model lifecycle, including data preprocessing, feature engineering, model training, and performance evaluation.
• Deep Learning & NLP: Utilize your expertise in deep learning with PyTorch to build and fine-tune complex neural networks. A key focus will be on Natural Language Processing (NLP) tasks, leveraging the Stanford NLP library for tasks such as sentiment analysis, named entity recognition, and coreference resolution.
• Large Language Models (LLMs): Stay at the forefront of AI by working with and fine-tuning large language models, specifically Llama 3.2. You will explore and implement its capabilities for tasks such as text generation, summarization, and question answering.
• Data Engineering & Analytics: Conduct in-depth data analysis and handle complex data pipelines. This includes robust JSON extraction, transformation of semi-structured data, and modeling complex relationships using graph databases like Neo4j to build knowledge graphs that feed our ML systems.
• API Development & Deployment: Develop and maintain scalable RESTful APIs to serve machine learning models in a production environment. Collaborate with our engineering teams to ensure seamless integration and contribute to our MLOps practices for scalability and reliability.
• Research & Innovation: Stay current with the latest advancements in machine learning, deep learning, and NLP. Proactively identify and champion new technologies and techniques that can enhance our products and processes.

Qualifications and Skills

• Educational Background: A Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field. A Bachelor's degree with significant relevant experience will also be considered.
• Programming Proficiency: Expert-level programming skills in Python and a strong command of its data science and machine learning ecosystem (e.g., Pandas, NumPy, Scikit-learn).
• Deep Learning Expertise: Proven experience in building and deploying deep learning models using PyTorch.
• Natural Language Processing (NLP): Demonstrable experience with NLP techniques and libraries, with specific expertise in using the Stanford NLP toolkit.
• Large Language Models (LLMs): Hands-on experience with and a strong understanding of large language models, including practical experience with models like Llama 3.2.
• Data Handling: Strong proficiency in handling and parsing various data formats, with specific experience in JSON extraction and manipulation.
• API Development: Solid experience in developing and deploying models via RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Graph Database Knowledge: Experience with graph databases, particularly Neo4j, and understanding of graph data modeling and querying (Cypher).
• Problem-Solving Skills: Excellent analytical and problem-solving abilities with a talent for tackling complex and ambiguous challenges.