ML Modeling and Deployment
- Train, fine-tune, and deploy machine learning models, including large language models (LLMs) to solve complex business problems.
- Develop and optimize Generative AI models, including diffusion models, LoRA, and advanced training techniques.
AI Workflows & Integration
- Design, implement, and optimize AI workflows leveraging LLMs, Generative AI, and third-party APIs.
- Build and deploy scalable AI pipelines to support real-time processing and large-scale data workflows.
- Transition AI prototypes into production-ready solutions in collaboration with cross-functional teams.
ML Infrastructure and Performance Optimization
- Scale data pipelines, optimize training and inference systems, and ensure reliability across all ML systems.
- Enhance system performance, scalability, and reliability to meet evolving customer and business needs.
- Monitor and improve deployed solutions based on feedback and performance metrics.
Backend Engineering and Cloud Deployment
- Develop backend features and automation tasks to integrate AI systems seamlessly into Karbons platform.
- Leverage cloud platforms (AWS, GCP, Azure) to design scalable GPU systems for AI/ML deployments.
Job Requirement
- 4+ Years of experience on AI project developemnt
- Strong understanding of fundamental ML algorithms, including transformer-based architectures.
- Proven experience working with and fine-tuning large language models (LLMs) like GPT, BERT, or similar frameworks.
- Hands-on experience deploying and optimizing Generative AI models with advanced knowledge of diffusion models, LoRA, and similar techniques.
- Strong proficiency in Python, Node.js with experience in frameworks like Express, TensorFlow and PyTorch.
- Cloud & Infrastructure: Proven expertise in cloud environments (AWS, GCP, Azure).
- Proficiency in building and integrating APIs and end-to-end AI workflows.
- Should be able to mentor team pf 3-5 Developers or Work on POCs as IC
