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.

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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.