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