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AI/ML & Generative AI Developer : About the Role : At Tecorb Technologies, we are building the future of intelligent systems by leveraging cutting-edge AI/ML and Generative AI technologies.

We are looking for a driven and technically grounded developer with 1-2 years of hands-on experience to join our AI/ML & Generative AI team. This role is suited for professionals who have moved beyond entry-level exposure and are ready to take ownership of AI/ML pipelines, contribute to real-world deployments, and grow into a senior AI engineer role. You will work on machine learning, deep learning, large language models (LLMs), and generative AI systems including GPT models, Transformers, diffusion models, and more.

Core AI/ML Knowledge Required : Machine Learning Fundamentals :

- Supervised, unsupervised, and semi-supervised learning paradigms
- Classical ML algorithms: Linear/Logistic Regression, Decision Trees, Random Forests, SVMs, Gradient Boosting (XGBoost, LightGBM)
- Model evaluation: cross-validation, bias-variance tradeoff, precision/recall, ROC-AUC
- Feature engineering, data preprocessing pipelines, and handling imbalanced datasets
- Hands-on experience with scikit-learn, NumPy, Pandas

Deep Learning & Neural Networks :

- Solid understanding of feedforward networks, backpropagation, and optimization (Adam, SGD, learning rate schedulers)
- Convolutional Neural Networks (CNNs) for image/vision tasks
- Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequential data
- Attention mechanisms and how they led to the Transformer architecture
- Experience with PyTorch and/or TensorFlow/Keras for model training and fine-tuning

Transformers & Large Language Models :

- Transformer Architecture — Self-attention, multi-head attention, positional encoding, encoder-decoder structure




- BERT (Bidirectional Encoder Representations from Transformers) — masked language modeling, fine-tuning for classification, NER, QA
- GPT Models (GPT-2, GPT-3, GPT-3.5, GPT-4) — causal language modeling, prompt engineering, in-context learning, and API integration
- T5, FLAN-T5 — encoder-decoder models for text-to-text tasks like summarization, translation, and QA
- LLaMA, Mistral, Falcon — open-source LLM architectures, weight loading, and inference optimization
- Hugging Face Transformers library — model loading, tokenization, pipelines, and Trainer API
- Tokenization strategies — BPE, WordPiece, SentencePiece
- Positional encodings — absolute, relative (ALiBi, RoPE)

Generative AI Systems :

- Generative Adversarial Networks (GANs) — basics of generator-discriminator training
- Variational Autoencoders (VAEs) — latent space, reconstruction loss, KL divergence
- Diffusion Models — forward/reverse diffusion, DDPM, Stable Diffusion, ControlNet
- Retrieval-Augmented Generation (RAG) — vector retrieval, chunking strategies, reranking
- Prompt engineering techniques — zero-shot, few-shot, chain-of-thought, ReAct
- Fine-tuning strategies — full fine-tuning, LoRA, QLoRA, instruction tuning, RLHF basics

NLP Fundamentals :

- Text preprocessing — tokenization, lemmatization, stopword removal, vectorization (TF-IDF, embeddings)
- Core NLP tasks — sentiment analysis,



Named Entity Recognition (NER), text classification, summarization, machine translation
- Word embeddings — Word2Vec, GloVe, FastText, and contextual embeddings from Transformers
- Semantic similarity, question answering, and dialogue systems

Key Responsibilities :

- Design, build, fine-tune, and deploy ML models and generative AI pipelines with mínimal supervision
- Implement end-to-end NLP systems — chatbots, content generators, summarizers, and RAG pipelines
- Perform data preprocessing, feature engineering, and dataset curation for training and evaluation
- Conduct model evaluation, benchmarking, error analysis, and iterative performance tuning
- Explore and implement reinforcement learning techniques (RLHF, reward modeling) to improve model interactions
- Build modular, scalable AI workflows using FastAPI, Docker, LangChain, and vector databases
- Stay current with AI research (arXiv, Hugging Face, OpenAI blogs) and prototype recent ideas rapidly
- Write clear documentation for experiments, code, models, pipelines, and datasets

Required Skills & Qualifications :

- Bachelor’s, Master’s, or PhD in Computer Science, Data Science, AI/ML, or a related field
- 1-2 years of hands-on professional or project experience with ML/AI systems
- Strong Python proficiency with ML libraries: scikit-learn, TensorFlow, PyTorch
- Solid understanding of Transformer architecture and attention mechanisms
- Practical experience working with GPT models via API (OpenAI, Azure OpenAI, Groq) or open-source equivalents
- Working knowledge of Hugging Face ecosystem (Transformers, Datasets, PEFT)
- Familiarity with LLM fine-tuning techniques: LoRA, QLoRA, or full fine-tuning
- Understanding of vector databases and embedding work

📌 Ai/ml & Generative Ai Developer (Noida)
🏢 Tecorb Technologies
📍 Noida