Senior Machine Learning Engineer
2 weeks ago
Job Description :
We are looking for a dynamic and driven Machine Learning Engineer with expertise in building and fine-tuning Large Language Models (LLMs) and a keen interest in expanding their skills in MLOps. The ideal candidate will have 4+ years of experience in designing and deploying machine learning models, a strong understanding of NLP and deep learning frameworks, and a proactive approach to learning and adopting MLOps practices for scalable and reliable model :
- Develop, fine-tune, and evaluate Large Language Models (LLMs) such as GPT, BERT, and other transformer based architectures.
- Apply advanced NLP techniques for tasks like text classification, summarization, question answering, and more.
- Preprocess, clean, and augment large datasets, ensuring the quality and relevance of training data.
- Implement optimization techniques to improve model efficiency and reduce computational costs.
- Stay updated on cutting-edge advancements in LLMs and integrate state-of-the-art methodologies into model development.
- Experiment with prompt engineering and fine-tuning techniques to optimize LLM performance for specific use cases.
- Collaborate with cross-functional teams to brainstorm and implement innovative AI-driven solutions.
- Collaborate with MLOps engineers to learn and contribute to the deployment of machine learning models into production environments.
- Build and maintain APIs for integrating ML models into applications using frameworks like FastAPI or Flask.
- Learn and implement model monitoring and governance frameworks to ensure model reliability and ethical compliance.
- Design data pipelines for feature engineering and real-time data processing.
- Collaborate with data engineers to automate data ingestion workflows and maintain high-quality datasets.
- Work on foundational MLOps tasks like model versioning, artifact tracking, and performance monitoring under guidance from experienced team members.
Requirements :
- Hands-on experience with Large Language Models (LLMs) and deep learning frameworks like TensorFlow, PyTorch, and HuggingFace Transformers.
- Strong understanding of Natural Language Processing (NLP) concepts, such as tokenization, embeddings, and transfer learning.
- Proficiency in Python and a willingness to learn tools like MLflow, KFP, and other MLOps frameworks.
- Eager to expand knowledge in MLOps practices, including automation, deployment pipelines, and monitoring systems.
- Effective problem-solving and communication skills to work collaboratively in a team-oriented environment.
- Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.
- Familiarity with cloud platforms such as Azure, AWS, or GCP for model deployment and scaling.
- Basic knowledge of DevOps tools like Docker and Kubernetes, with a willingness to develop these skills further.
- Exposure to data visualization tools like Looker Studio or Power BI- for presenting model insights.
(ref:hirist.tech)-
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