AI & ML Engineer

Job Summary We are looking for an experienced AI & Generative AI Developer who can work across the AI spectrum—from classical machine learning models to cutting-edge Generative AI applications. The role demands strong experience in building ML models using regression, classification, and tree-based algorithms, along with hands-on exposure to LLMs and generative frameworks like GPT, Stable Diffusion, and LangChain. Key Responsibilities 🔹 Classical AI/ML Design and implement supervised and unsupervised ML models including: Linear Regression, Logistic Regression Decision Trees, Random Forest, XGBoost Naive Bayes, K-Means, SVM, PCA, etc. Preprocess and analyse structured/tabular datasets Evaluate models using metrics like accuracy, precision, recall, ROC-AUC, and RMSE Build predictive models , deploy them into production, and monitor performance Collaborate with business teams to translate requirements into ML use cases 🔹 Generative AI (GenAI) Build and fine-tune LLMs (e.g., GPT, LLaMA, PaLM) for summarisation, Q&A, document generation, etc. Implement prompt engineering , RAG pipelines , and vector database integrations Use libraries like Hugging Face Transformers, LangChain, and LlamaIndex Develop APIs to expose GenAI models in real-time apps Optimise model inference using quantisation, batching, etc. Ensure safe, explainable, and bias-free output in alignment with AI ethics guidelines Required Skills & Qualifications Bachelor’s or Master’s in Computer Science, Data Science, Statistics, or related field Strong programming skills in Python , with experience in NumPy, Pandas, Scikit-learn Proficiency in classical ML algorithms (regression, trees, naive Bayes, etc.) Experience with LLM frameworks like OpenAI API, Hugging Face, and LangChain Understanding of transformer architecture , NLP, embeddings, and tokenisation Familiarity with REST API development using FastAPI/Flask Exposure to cloud platforms (AWS/GCP/Azure) and Docker/Kubernetes Preferred / Nice to Have Experience with deep learning (TensorFlow, PyTorch) Exposure to image/audio/video generation using models like DALL·E, Stable Diffusion, Whisper Familiarity with RAG , LLMOps , and vector stores (FAISS, Pinecone, Weaviate) Knowledge of MLOps pipelines , model monitoring , and CI/CD for ML