AI/ML & Generative AI Developer
Job Description
About the Role Experience: 1-2 years
University/Education: Graduated from a reputable college/university or has worked on projects they are proud of and can confidently showcase.
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. If you connect dots, understand systems holistically, and are passionate about building intelligent applications , we want you.
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 Transformers are the backbone of modern AI. Candidates must have strong knowledge of: 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 minimal supervision Integrate pre-trained models (GPT-4, LLaMA, Mistral, Stable Diffusion) into production applications via APIs or custom backends 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 new 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 workflows (FAISS, ChromaDB, Pinecone) Knowledge of APIs, JSON, and RESTful application design Proficiency with Git, Jupyter Notebooks, and Linux command-line tools Nice to Have (Not Mandatory) Familiarity with MLOps tools: Weights & Biases, MLflow, DVC, or TensorBoard Basic knowledge of reinforcement learning What You’ll Get Hands-on experience on real AI/ML projects and production-ready systems from day one Mentorship and career development from senior AI/ML engineers and researchers Access to state-of-the-art tools, compute infrastructure, and paid API credits Opportunity to contribute to high-impact solutions in AI automation, GenAI workflows, and enterprise applications A learning-driven, inclusive, and collaborative work environment Fast growth track with exposure to client-facing work, research, and system design
