Machine Learning Engineer-Large language Modules

Role:

Machine Learning Engineer – Large Language Models

Roles And Responsibilities:

• 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 technical solutions.
• 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.

Technical Skills:

• 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.

Soft Skills:

• Excellent communication and collaboration skills, with an ability to work effectively across interdisciplinary teams.
• Strong analytical and problem-solving skills, with attention to detail and a passion for continuous learning.
• Ability to work independently and manage multiple projects in a fast-paced, dynamic environment.

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.
• Professional development opportunities, including access to conferences, workshops, and training programs.
• A collaborative, inclusive work culture that values innovation and teamwork

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.

Primary skills (Must have):

• Python
• PyTorch, TensorFlow, Hugging Face Transformers
• Familiarity in cloud platforms-AWS, GCP, Azure
• docker, kubernetes

Interview Details:

•      Video screening with HR
•      L1 - Technical Interview
•      L2 - Technical and HR Round

Note:

Working Hours:

Working days