i am Rakesh kumar
An aspiring AI / Generative AI Engineer with full-stack exposure, skilled in building, deploying, and governing intelligent systems. I combine strong foundations in machine learning, deep learning, NLP, speech processing, and large language models with practical experience in full-stack development, cloud deployment, and Responsible AI practices to deliver scalable, ethical, and production-ready AI solutions.
LEARNINGs
Generative AI Engineer | LLMs & RAG | Prompt Engineering | NLP & Speech | Agentic AI | Full-Stack & Cloud
Core Deep Learning Foundations
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Strong foundation in deep learning theory and practice, including neural networks, multilayer perceptrons, backpropagation, and gradient-based optimization
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Experience with activation functions, loss functions, regularization, dropout, and hyperparameter tuning
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Optimization techniques: SGD with momentum, RMSProp, Adam
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Solid understanding of overfitting control and model generalization
Generative AI & Large Language Models
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In-depth understanding of Generative AI paradigms, including statistical language models and neural language models
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Strong knowledge of Transformer architectures, encoder–decoder models, and self-attention mechanisms
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Experience with LLM families: BERT, GPT, T5, ELMo
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Text generation strategies:
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Greedy decoding
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Beam search
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Sampling methods (Top-k, Top-p / Nucleus sampling)
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Knowledge of multimodal transformers and cross-modal learning
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Model evaluation techniques for generative tasks using BLEU and ROUGE
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Prompt Engineering & Advanced GenAI Systems
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Prompt engineering fundamentals and prompt design principles
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Techniques:
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Zero-shot, one-shot, and few-shot prompting
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Chain-of-Thought (CoT) reasoning
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Graph-based and automatic prompt engineering (APE)
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Retrieval-Augmented Generation (RAG):
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RAG pipelines and core components
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Dense vector retrieval and embedding-based search
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Advanced RAG strategies including FLARE
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Vector databases and semantic search using FAISS and Pinecone
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Parameter-efficient fine-tuning concepts for LLMs
NLP & Generative Text Systems
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Strong foundation in Natural Language Processing, text preprocessing, and language modeling
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Word and contextual embeddings:
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Word2Vec, GloVe, FastText
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Transformer-based text classification, summarization (extractive & abstractive)
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Machine translation using encoder–decoder architectures
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Information extraction, semantic role labeling, and dependency parsing
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Dense vector-based information retrieval integrated with generative models
Speech & Multimodal Generative AI
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Speech signal processing and feature extraction using spectrograms
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Automatic Speech Recognition (ASR):
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CTC-based, attention-based, and transformer-based models
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Experience with wav2vec and Whisper
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Neural Text-to-Speech (TTS) systems and sequence-to-sequence speech synthesis
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Voice conversion and style transfer
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Speaker analytics:
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Speaker verification and identification
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Speaker embeddings (i-vectors, x-vectors, d-vectors)
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Multimodal emotion recognition using text + speech
Reinforcement Learning & Agentic Generative AI
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Reinforcement Learning fundamentals:
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Agents, environments, rewards, MDPs
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Deep RL algorithms:
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Q-Learning, DQN, Actor–Critic, PPO
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Exposure to model-based RL approaches (MuZero, Dreamer)
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Multi-agent learning concepts
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Agentic AI systems using LLMs and LangGraph for autonomous reasoning and decision-making workflows
Reinforcement Learning & Agentic Generative AI
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Responsible AI principles: fairness, accountability, transparency, privacy, and safety
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Bias detection and mitigation strategies in generative models
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Explainable AI (XAI) techniques:
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LIME, SHAP, counterfactual explanations
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Model documentation and auditing:
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Model Cards, Datasheets, FactSheets
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Privacy-preserving ML:
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Differential privacy, federated learning
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Awareness of global AI regulations:
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GDPR, DPDP Act, EU AI Act
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Ethical considerations in Generative AI, synthetic media, and autonomous systems
Full-Stack Development & GenAI Deployment
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Working knowledge of full-stack development:
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Frontend: React
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Backend: Node.js, MongoDB
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Building AI-powered services using FastAPI
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Model optimization techniques including quantization
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Containerization using Docker
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Cloud deployment using AWS
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LLM deployment and inference using:
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OpenAI APIs
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Hugging Face
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vLLM
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Ollama
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Understanding of scalable, reliable, and production-ready GenAI systems
