Machine Learning Engineer

Job Description:

Job Duties


* Develop and deploy machine learning models for traditional ML, NLP, and Generative AI solutions.


* Design, build, and maintain MLOps pipelines for seamless model deployment and continuous updates.


* Collaborate with stakeholders to gather requirements and translate them into technical solutions.


* Create scalable data pipelines for data collection, cleaning, and feature engineering.


* Implement best practices for code quality, version control, testing, and documentation.


* Conduct A/B testing and automated tests to validate model performance and reliability.


* Participate in code reviews and provide mentorship on machine learning practices.


* Monitor and optimize model performance in production environments.


* Stay updated on industry trends to continuously improve and innovate solutions.

Qualifications


* Bachelor's or Master's degree in computer science, Data Science, or a related field.


* Extensive experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn, including model training, tuning, and deployment.


* Experience in building Retrieval-Augmented Generation (RAG) applications, leveraging LLMs to augment search and retrieval tasks.


* Proficiency in big data technologies, specifically distributed computing frameworks such as PySpark, for handling large-scale data processing and analysis.


* Hands-on experience with natural language processing (NLP) libraries like NLTK, SpaCy, and Hugging Face's Transformers, and familiarity with Large Language Models (LLMs) and their applications.


* Experience with SQL and NoSQL databases, with strong skills in database design, query optimization, and data retrieval.


* Strong experience with cloud platforms, preferably Microsoft Azure, including Azure Machine Learning, Azure Databricks, and Azure DevOps for CI/CD pipeline automation.


* Proven expertise in Python programming, with experience in Django or Flask for designing and implementing RESTful APIs to deploy and integrate models.


* Solid understanding of MLOps practices, including model lifecycle management, deployment, monitoring, and continuous integration.


* Strong problem-solving abilities and the ability to work independently, manage complex projects, and deliver results in fast-paced environments.

Equivalent experience and skills are welcome.

We encourage all qualified candidates to apply!