Machine Learning Engineer

3-5 Years

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Early Applicant

Job Description

Design, develop, and deploy cutting-edge LLMs for diverse NLP applications.
Optimize and fine-tune large-scale language models using frameworks like TensorFlow, PyTorch, or JAX.
Research and implement advancements in LLMs, including transformer architectures and reinforcement learning techniques.
Work closely with data scientists, software engineers, and product managers to integrate LLMs into production systems.
Build and maintain scalable machine learning pipelines for training and inference.
Ensure LLMs are efficient, high-performing, and scalable for real-world applications.
Evaluate AI models using key NLP metrics and refine them based on experimental results.
Stay updated with the latest AI research and contribute to open-source projects where relevant.
Implement responsible AI principles, including fairness, explainability, and ethical considerations.
Document AI model architectures, training methodologies, and deployment strategies.

Minimum Qualifications

Bachelor's, Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
Minimum 3-5 years of experience in machine learning, deep learning, or natural language processing roles.
Strong proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, or JAX.
Hands-on experience with large-scale language models, including transformers, GPT-based models, and BERT-style architectures.
Experience with cloud platforms (AWS, Google Cloud, Azure) and ML model deployment.
Knowledge of MLOps practices, including model versioning, monitoring, and automation.
Experience with large-scale datasets, data preprocessing, and distributed computing frameworks (e.g., Spark, Ray).
Solid understanding of deep learning architectures, optimization techniques, and reinforcement learning.
Strong problem-solving skills and ability to conduct independent research.
Excellent collaboration and communication skills for cross-functional teamwork.
Experience with prompt engineering, fine-tuning LLMs, and retrieval-augmented generation (RAG) is a must.
Contributions to AI research publications or open-source projects are highly desirable.