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