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model pipelines, training infrastructure, feature stores, and serving systems.\n\n- Build and fine-tune Large Language Models (LLMs) and Vision-Language Models (VLMs) for domain-specific applications (e.g., RAG, agents, multimodal understanding).\n\n- Design and deploy production-grade computer vision systems (object detection, segmentation, OCR, video analytics) at scale.\n\n- Develop and maintain classical ML models (regression, classification, clustering, time-series forecasting, recommendation engines) where appropriate.\n\n- Architect scalable, low-latency model serving infrastructure for both batch and real-time inference workloads.\n\n- Establish best practices for experiment tracking, model versioning, A/B testing, reproducibility, and documentation across the team.\n\n- Mentor and technically lead a team of data scientists and ML engineers; conduct architecture reviews, design discussions, and code reviews.\n\n- Collaborate closely with Product, Engineering, and Business teams to translate complex business problems into well-defined AI solutions.\n\n- Evaluate and integrate emerging AI technologies, frameworks, and research papers into the product roadmap.\n\n- Champion responsible AI - fairness, bias detection, explainability, and compliance with data privacy regulations.\n\n- Communicate technical strategies, trade-offs, and results to senior leadership and non-technical stakeholders.\n\nRequired Qualifications :\n\n- 8+ years of hands-on experience in data science, machine learning, or AI engineering, with at least 2 - 3 years in a tech lead or senior IC capacity.\n\n- Deep expertise in LLMs - fine-tuning (LoRA, QLoRA, PEFT), prompt engineering, RAG pipelines, embedding models, and LLM evaluation frameworks.\n\n- Strong hands-on experience with Vision-Language Models (VLMs) such as LLaVA, GPT-4V, Gemini, or similar multimodal architectures.\n\n- Proven track record in computer vision - CNNs, transformers (ViT, DETR, SAM), object detection (YOLO, Faster R-CNN), segmentation, OCR, and video understanding.\n\n- Solid command of classical ML techniques - ensemble methods, gradient boosting (XGBoost, LightGBM), Bayesian methods, and time-series modeling.\n\n- Strong proficiency in Python and ML/DL frameworks (PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or equivalent).\n\n- Production ML experience - 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