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