Data Scientist (In Person Interview)

Job Title: Data Scientist (In-Person Interview) Location: Remote Duration: Long-Term Visa: Any visa is Fine(Need 4+ years of experience in Data Scientist) Interview process: 1st round virtual and 2nd round (F2F mandatory) End Client: Working in Implementation Project Note: Need at least 4+ years of experience complete into Data Scientist. Note: while sharing the resume please share writeup for data scientist for your experience. Please share resumes at Akhil@tror.ai Key Responsibilities • Lead end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, Llama, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems. • Architect and implement Graph RAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding. • Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval. • Develop semantic retrieval systems with advanced document segmentation and indexing strategies. • Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training. • Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals. • Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments. • Designing and implementing analytical frameworks. • Developing predictive and prescriptive models. Preferred Qualifications • PhD or master’s degree in computer science, Machine Learning, or related field. • 4+ years of experience in applied AI/ML, with a strong track record of delivering production-grade models. Deep Expertise In • LLM training and fine-tuning (e.g., GPT, Llama, Mistral, Qwen) • Graph-based retrieval systems (Graph RAG, knowledge graphs) • Embedding models (e.g., BGE, E5, SimCSE) • Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus) • Document segmentation and preprocessing (OCR, layout parsing) • Distributed training frameworks (NCCL, Horovod, DeepSpeed) • High-performance networking (InfiniBand, RDMA) • Model fusion and ensemble techniques (stacking, boosting, gating) • Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms) • Symbolic AI and rule-based systems • Meta-learning and Mixture of Experts architectures • Reinforcement learning (e.g., RLHF, PPO, DPO) • Experience applying causal inference techniques (e.g., causal impact analysis, uplift modeling, DoWhy) to marketing and engagement analytics. • Exercise independent judgment in methods, techniques, and evaluation criteria on data science projects, overseeing the end-to-end process from problem definition to model implementation. • Proficiency with programming languages like Python, R, and SQL. • Strong background in predictive modeling, classification, segmentation, and optimization. • Extensively worked in any Cloud environment. Bonus Skills • Familiarity with regulatory and compliance frameworks in AI deployment. • Contributions to open-source AI projects or published research. And/Or ability to take research papers to poc – production.