The incumbent will solve complex business problems through predictive modeling, deep learning, and generative AI research. Responsibilities span from mathematical formulation and prototype development to training large-scale models (1B+ parameters) and rigorous evaluation. This position requires deep expertise in machine learning theory, neural network architectures, and statistical modeling, with emphasis on creating novel solutions rather than infrastructure management.
CORE RESPONSIBILITIES :
– Design and architect neural network models including Transformers, CNNs, RNNs, and hybrid architectures; make decisions on layer configurations, attention mechanisms, activation functions, and connectivity patterns for optimal performance.
– Develop and implement training algorithms and optimization strategies including custom loss functions, learning rate schedules, gradient clipping, and regularization techniques to ensure stable convergence and generalization.
– Fine-tune pre-trained foundation models (LLaMA, Mistral, BERT, GPT, T5) using Parameter- Efficient Fine-Tuning(PEFT) methods including LoRA, QLoRA, Prefix Tuning, and AdaLoRA for domain-specific applications.
– Implement Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI methodologies; design reward models, policy optimization algorithms (PPO, DPO), and human preference learning systems.
– Engineer high-quality training datasets through data collection strategies, cleaning pipelines, augmentation techniques,and synthetic data generation; ensure data representatives and bias mitigation.
– Design and execute comprehensive model evaluation frameworks including statistical significance testing, cross-validation strategies, benchmark dataset evaluation (MMLU, HumanEval, GLUE, SuperGLUE), and custom metrics development.
– Develop Retrieval-Augmented Generation (RAG) architectures including embedding model selection, retrieval algorithms, context integration strategies, and relevance scoring mechanisms to enhance model accuracy.
– Optimize model architectures for efficiency through knowledge distillation, model pruning, quantization-aware training,and neural architecture search (NAS) without compromising accuracy.
– Perform rigorous statistical analysis and hypothesis testing on model outputs; identify failure modes, error analysis, and edge cases requiring architectural improvements.
– Collaborate with domain experts to translate business requirements into mathematical formulations and ML problem statements; define target variables, feature spaces, and success criteria.
– Mentor junior researchers and engineers on machine learning theory, algorithmic best practices, experimental design, and research methodologies; conduct code reviews for model implementations.
– Document research findings, model architectures, training methodologies, and experimental results in technical reports; publish papers in conferences or journals and present at technical forums.
– Analyze model interpret ability and explain ability using attention visualization, SHAP values, LIME, and gradient-based attribution methods to ensure transparency in AI decision-making.
ESSENTIAL QUALIFICATIONS & EXPERIENCE :
Educational Qualifications :
– Bachelor’s degree (B.E./B.Tech) in Computer Science, Engineering, Mathematics, Statistics, Physics, or related quantitative field from a recognized university.
– Master’s degree (M.Tech/MS) or PhD in Machine Learning, Artificial Intelligence, Computer Science, or related field highly desirable; exceptional candidates with Bachelor’s degree and significant research experience may be considered.
– Strong foundation in linear algebra, calculus, probability theory, statistics, and optimization theory essential.
Experience Requirements :
– Minimum 5-9 years of research and development experience in applied machine learning, with demonstrable expertise in designing and training neural networks.
– Extensive experience in at least two domains : Natural Language Processing (NLP), Computer Vision, Speech Recognition, Recommendation Systems, or Reinforcement Learning
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