Machine Learning Engineer - Data Modeling

Responsibilities :

- Design, develop, and implement machine learning models and algorithms, including creating novel models from scratch based on business requirements and data.

- Expertise in training, fine-tuning, and rigorously evaluating machine learning models (including ASR and LLMs like Llama, Deepseek, and Mistral) to ensure optimal performance and alignment with business objectives, utilizing metrics such as accuracy, precision, recall, F1

score, and ROC-AUC.

- End-to-end experience in deploying machine learning models into production environments,

including performance benchmarking and testing to ensure reliability and scalability.

- Collaborate effectively with cross-functional teams to understand business needs and translate them into impactful AI/ML solutions.

- Iteratively optimize and improve the performance of existing machine learning models.

- Conduct proactive research to identify and evaluate new AI/ML methodologies and their

potential application within the organization.

- Provide mentorship and technical guidance to junior engineers, fostering their growth in

AI/ML.

- Continuously stay abreast of the latest advancements in AI/ML technologies, methodologies,

and industry best practices.

Requirements :

- Core Machine Learning : Strong theoretical and practical understanding of supervised, unsupervised, and reinforcement learning paradigms.

- Model Development and Deployment : Proven experience in the complete lifecycle of

machine learning model development, encompassing data preprocessing, feature engineering,

model selection, training, evaluation, and deployment.

- Deep Learning Expertise : Proficiency in utilizing deep learning frameworks such as TensorFlow, PyTorch, and/or Keras for building and deploying complex models.

- Natural Language Processing and Generation (Crucial Emphasis) : Extensive experience with Natural Language Processing (NLP) techniques and tools, with a strong focus on developing and working with Large Language Models (LLMs) including GPT, BERT, Llama, Deepseek, and Mistral.

- Programming Proficiency : Expert-level proficiency in Python; familiarity with other

programming languages such as Java or C++ is advantageous.

- Essential Libraries : Hands-on experience with key ML libraries and tools, including Scikit-learn, Pandas, NumPy, and SciPy for data manipulation, analysis, and model building.

- Cloud ML Platforms : Practical experience with cloud-based ML platforms such as AWS SageMaker, Google Cloud AI Platform, or Azure Machine Learning for developing, training, and deploying models at scale.

- MLOps Best Practices : Solid understanding of MLOps principles and practices for ensuring robust model versioning, comprehensive monitoring, and seamless continuous integration/continuous deployment (CI/CD) pipelines.

- Data Visualization : Proficiency in utilizing data visualization tools such as Matplotlib, Seaborn, or Tableau to effectively communicate insights and model performance.

- Hugging Face Ecosystem (Highlighting Relevance) : Significant familiarity and practical

experience with the Hugging Face ecosystem, including the Transformers library for leveraging pre-trained models, the Datasets library for efficient data handling and processing, and the Model Hub for model sharing and discovery.