Job
Description
Key Responsibilities Design, develop, and deploy AI solutions that span both traditional ML models and GenAI-based systems . Build machine learning pipelines using algorithms like linear/logistic regression, decision trees, SVMs, random forests, XGBoost , clustering (K-means, DBSCAN), and time series forecasting . Analyze datasets to derive meaningful insights and build predictive models to solve business problems. Work on GenAI applications using LLMs, including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG). Develop and integrate LLM-based features using frameworks like LangChain, Hugging Face Transformers, or OpenAI API. Collaborate with data, product, and engineering teams to define and implement AI-driven functionalities . Apply statistical modeling and inference techniques for feature selection, model evaluation, and data exploration. Optimize performance of ML and GenAI models through hyperparameter tuning, cross-validation , and error analysis. Design and maintain data pipelines and ML workflows using tools like Airflow, DVC , or MLflow . Deploy models into production with appropriate MLOps practices, ensuring monitoring, retraining , and version control . Research and evaluate advancements in both traditional ML and LLM-based AI . Skills & Experience 3-6 years of experience in AI/ML using Python. Proficient in machine learning algorithms (classification, regression, clustering, dimensionality reduction, ensemble methods). Hands-on experience with GenAI/LLM applications (prompt design, RAG, fine-tuning, etc.). Familiarity with data preprocessing, feature engineering , and working with structured and unstructured data . Proficient in Python ML libraries : Scikit-learn, XGBoost, LightGBM, Pandas, NumPy. Experience with deep learning frameworks : PyTorch or TensorFlow. Familiarity with vector databases (e.g., FAISS, Pinecone) and LLM orchestration tools (LangChain, Hugging Face). Experience in model evaluation techniques , including AUC-ROC, precision-recall, RMSE, etc. Familiarity with cloud AI services (AWS SageMaker, GCP AI Platform, or Azure ML). Solid understanding of MLOps tools : MLflow, Docker, Git, CI/CD pipelines. Strong analytical, communication, and collaboration skills.
