Senior Machine Learning Engineer - NLP/Deep Learning (5-6 yrs)

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 deployment.

Responsibilities :

- 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.