Job Title

Push the Boundaries of LLM Performance – Lead Post-Training Innovation in Life Sciences AIAt Dizzaroo Pvt Ltd, we are building AI-powered platforms that transform the pharmaceutical and life sciences industry — from drug discovery to digital pathology to clinical trial automation. Large Language Models (LLMs) are at the core of several of our applications. But we’re not simply plugging in APIs — we’re customizing, aligning, and post-training these models to handle high-stakes, domain-specific biomedical and regulatory content. We are looking for a seasoned AI/ML expert who understands the full post-training lifecycle of LLMs — not just running a fine-tuning job in a managed platform, but architecting and executing large-scale post-training projects from dataset curation to deployment. PositionAI/ML Expert – Post-Training of Large Language Models (LLMs) Location:Pune (Hybrid; remote options for exceptional circumstances) Key ResponsibilitiesLead post-training workflows for domain-specific LLMs, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and other alignment methods. Design and implement large-scale training pipelines — from data preprocessing to distributed training and evaluation. Curate domain-specific datasets for biomedical, regulatory, and clinical contexts. Experiment with parameter-efficient fine-tuning techniques (LoRA, QLoRA, adapters) and prompt optimization strategies. Benchmark model performance on domain-relevant evaluation suites and iterate to improve accuracy, reliability, and explainability. Collaborate with product and domain teams to ensure model outputs meet regulatory-grade precision. QualificationsProven hands-on experience with post-training of LLMs beyond basic managed-service fine-tuning. Proficiency in PyTorch and transformers libraries (Hugging Face, DeepSpeed, Megatron-LM, or similar). Experience in distributed training, large-scale GPU/TPU clusters, and optimization for high-parameter models. Strong understanding of LLM architecture, tokenization strategies, and memory optimization techniques. Experience with biomedical or technical text corpora is a plus. Bachelor’s/Master’s/PhD in Computer Science, AI/ML, or related field. What We ValueEnd-to-end ownership — you’ve run a project from dataset to deployed model. Curiosity and deep technical engagement with model internals. The ability to explain complex ML workflows to both engineers and non-technical stakeholders. Comfort working in ambiguous, fast-moving R&D environments. Why Join UsShape the next generation of domain-specific LLMs for life sciences. Work at the cutting edge of AI + healthcare with a highly interdisciplinary team. Opportunity to set technical direction for high-impact AI initiatives. How to ApplySend the following to dhirajg@ Cover Letter– Describe your most significant LLM post-training project. CV/Resume . Links to code, research, or demosthat showcase your work.