• Design and Develop AI-Powered Solutions: Architect and implement scalable AI/ML systems, focusing on Large Language Models (LLMs) and other deep learning applications.
• End-to-End Model Development: Lead the entire lifecycle of AI models—from data collection and preprocessing to training, fine-tuning, evaluation, and deployment.
• Fine-Tuning & Customization: Leverage techniques like LoRA (Low-Rank Adaptation) and Q-LoRA to efficiently fine-tune large models for specific business applications.
• Reasoning Model Implementation: Work with advanced reasoning models such as DeepSeek-R1, exploring their applications in enterprise AI workflows.
• Data Engineering & Dataset Creation: Design and curate high-quality datasets optimized for fine-tuning AI models, ensuring robust training and validation processes.
• Performance Optimization & Efficiency: Optimize model inference, computational efficiency, and resource utilization for large-scale AI applications.
• MLOps & CI/CD Pipelines: Implement best practices for MLOps, ensuring automated training, deployment, monitoring, and continuous improvement of AI models.
• Cloud & Edge AI Deployment: Deploy and manage AI solutions in cloud environments (AWS, Azure, GCP) and explore edge AI deployment where applicable.
• API Development & Microservices: Develop RESTful APIs and microservices to integrate AI models seamlessly into enterprise applications.
• Security, Compliance & Ethical AI: Ensure AI solutions comply with industry standards, data privacy laws (e.g., GDPR, HIPAA), and ethical AI guidelines.
• Collaboration & Stakeholder Engagement: Work closely with product managers, data engineers, and business teams to translate business needs into AI-driven solutions.
• Mentorship & Technical Leadership: Guide and mentor junior engineers, fostering best practices in AI/ML development, model fine-tuning, and software engineering.
• Research & Innovation: Stay updated with emerging AI trends, conduct experiments with cutting-edge architectures and fine-tuning techniques, and drive innovation within the team.

Basic Qualifications

• A master's degree or PhD in Computer Science, Data Science, Engineering, or a related field
• Experience: 5-8 Years
• Strong programming skills in Python
• Good understanding of machine learning fundamentals
• Hands-on experience with Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit-learn)
• Familiar with frontend development and frameworks like React
• Basic knowledge of LLMs and transformer-based architectures is a plus

Preferred Qualifications

• Excellent problem-solving skills and an eagerness to learn in a fast-paced environment
• Strong attention to detail and ability to communicate technical concepts clearly

‍

Why Gruve At Gruve, we foster a culture of innovation, collaboration, and continuous learning. We are committed to building a diverse and inclusive workplace where everyone can thrive and contribute their best work. If you’re passionate about technology and eager to make an impact, we’d love to hear from you.

Gruve is an equal opportunity employer. We welcome applicants from all backgrounds and thank all who apply; however, only those selected for an interview will be contacted.