We are seeking a talented and motivated Machine Learning Engineer with a deep understanding of Large Language Models (LLMs) to join our team. In this role, you will be responsible for designing, implementing, and optimizing large-scale language models to power AI-driven applications.
You will work closely with a cross-functional team of data scientists, software engineers, and product managers to develop innovative solutions that enhance user experience and drive business impact.
Design, develop, and deploy large-scale language models for a range of NLP tasks such as text generation, summarization, question answering, and sentiment analysis.
Fine-tune pre-trained models (e.g., GPT, BERT, T5) on domain-specific data to optimize performance and accuracy.
Collaborate with data engineering teams to collect, preprocess, and curate large datasets for training and evaluation.
Experiment with model architectures, hyperparameters, and training techniques to improve model efficiency and performance.
Develop and maintain pipelines for model training, evaluation, and deployment in a scalable and reproducible manner.
Implement and optimize inference solutions to ensure models are performant in production environments.
Monitor and evaluate model performance in production, making improvements as needed.
Document methodologies, experiments, and findings to share with stakeholders and other team members.
Stay current with advancements in LLMs, NLP, and machine learning, and apply new techniques to existing projects.
Collaborate with product managers to understand project requirements and translate them into
Screening Criteria:
Immediate joiners
Min 3 to 5 years of Experience
Location: Remote
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
Primary skills (Must have):
3+ years of experience in machine learning and natural language processing.
Proven experience working with LLMs (such as GPT, BERT, T5, etc.) in production environments.
Demonstrated experience fine-tuning and deploying large-scale language models.
Proficiency in Python and experience with ML libraries and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, etc.
Strong understanding of deep learning architectures (RNNs, CNNs, Transformers) and hands-on experience with Transformer-based architectures.
Familiarity with cloud platforms (AWS, GCP, Azure) and experience with containerization tools like Docker and orchestration with Kubernetes.
Experience with data preprocessing, feature engineering, and data pipeline development.
Knowledge of distributed training techniques and optimization methods for handling large datasets.
Preferred Qualifications:
Experience with prompt engineering and techniques to maximize the effectiveness of LLMs in various applications.
Knowledge of ethical considerations and bias mitigation techniques in language models.
Familiarity with reinforcement learning, especially RLHF (Reinforcement Learning from Human Feedback).
Experience with model compression and deployment techniques for resource-constrained environments.
Contributions to open-source projects or publications in reputable machine learning journals
