Job Description

Company Description In a rapidly evolving AI-first market, GraaS stands at the forefront of innovation. GraaS is obsessed on creating asymmetrical opportunities for its enterprise customers' growth. Joining our team means being part of a transformative journey that bridges the gap between the expected and the extraordinary. This is a hands-on, cross-functional role where your work will directly impact our vision. What you?ll do: Develop & Optimize LLMs : Design and optimize transformer-based models to understand images, videos, and text, and optimize for real-time inference. Pre-training & Fine-tuning : Own the full training pipeline, from pre-training to fine-tuning for enterprise domain and use cases.? Model Compression & Optimization : Apply techniques like distillation, quantization, and pruning to reduce model size and latency, enabling efficient edge deployment. Leverage Open-Source & Innovate : Use and extend state-of-the-art open-source models. Prototype new architectures and training methods to advance GraaS? multimodal AI research. Cross-Team Collaboration : Work with engineering and product teams to integrate models into the platform. Iterate based on real-world feedback and deployment data to improve performance. Research and Experimentation : Stay current with NLP, GAMA and multimodal AI research. Design experiments to test new algorithms and continually enhance our core AI systems. What you?ll bring: LLM Expertise & Application: deep understanding of how LLMs work with proven ability to apply them effectively, push their performance, and optimize through advanced prompt engineering techniques. Python Performance Mastery: strong experience with Python beyond surface level, expert with asynchronous programming, event loop optimization, and performance tuning for high-scale applications. Cross-stack Proficiency: Proficient in Python, deep learning frameworks and LLM integrations (OpenAI, Anthropic, HuggingFace, etc.). Knows how to production-ize ML models (data prep, evaluation, vector DBs, retrieval, etc.) Model training and Optimization: proven skills in fine-tuning on large datasets and applying distillation, quantization, or similar techniques. Multi-modal models experience is a plus. Strong Problem-solving: quick prototyping, diagnosing failure cases, and iterating on solutions Startup experience preferred: A minimum of 7+ years in per-revenue or early-stage startup. Comfortable with ambiguity, fast iteration, and owning projects end-to-end Ph.D. or Master?s in CS, EE, or related field, with a strong foundation in AI/ML (Ph.D. preferred or Master?s with strong portfolio) Salary and Equity: At GraaS, we take a market-based approach to compensation. Final offers are based on job-related skills, experience, location, and internal equity. Base salary is just one part of our total rewards package, including stock options and the opportunity to share in the company?s growth. The starting base salary range for this role in Bengaluru: Rs. 35 Lpa ? Rs. 55 Lpa