AI ML Developer

Key Responsibilities

• Design, develop, train, and optimize Machine Learning and Deep Learning models.
• Analyze structured and unstructured datasets to identify business insights and opportunities.
• Build and maintain data pipelines for model training and inference.
• Develop AI solutions using NLP, Computer Vision, Recommendation Systems, or Predictive Analytics.
• Deploy ML models using cloud platforms and MLOps best practices.
• Perform feature engineering, model evaluation, hyperparameter tuning, and performance optimization.
• Collaborate with Data Scientists, Software Engineers, and business stakeholders.
• Monitor model performance and retrain models as required.
• Document model architecture, experiments, and deployment processes.

Requirements & Tech Stack

• Strong programming skills in Python.
• Familiarity with Machine Learning libraries such as Scikit-learn, XGBoost, TensorFlow, Keras, or PyTorch.
• Good understanding of Supervised & Unsupervised Learning, Deep Learning, NLP, and Computer Vision.
• Familiarity with data analysis tools like Pandas, NumPy, Matplotlib.
• Knowledge of SQL and working with large datasets.
• Familiarity with REST APIs and model deployment.
• Familiarity with Git version control.
• Exposure to Generative AI, LLMs, LangChain, RAG, Vector Databases (Pinecone, ChromaDB, FAISS).
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of Docker, Kubernetes, CI/CD, and MLOps tools.
• Familiarity with MLflow, Airflow, or Kubeflow.
• Familiarity with prompt engineering and AI agent frameworks.