Ai Ml Engineer

Role & responsibilities

• • • • • • • • • • • • • Design, develop, train, evaluate, and optimize Machine Learning and AI models. Develop AI/ML solutions using Python and relevant ML/DL frameworks. Apply machine learning algorithms, statistical techniques, feature engineering, and model evaluation methodologies to solve business problems. Work with supervised and unsupervised learning, predictive modeling, classification, regression, clustering, and other ML techniques. Develop and implement Deep Learning / Neural Network models using frameworks such as TensorFlow or PyTorch. Work on NLP, LLMs, Generative AI, and Agentic AI use cases where applicable. Perform data preprocessing, exploratory data analysis, feature engineering, model training, validation, and performance optimization. Integrate AI/ML models with applications, APIs, data pipelines, and enterprise systems. Deploy and monitor AI/ML models in production environments. Work with cloud-based AI/ML platforms such as Google Cloud Vertex AI, AWS SageMaker, Azure ML, or equivalent. Collaborate with engineering, product, and data teams to convert business requirements into scalable AI/ML solutions. Conduct model performance analysis, troubleshooting, experimentation, and continuous optimization. Follow an AI-first approach to identify opportunities for intelligent automation and data-driven solutions

Preferred candidate profile

• • • • • • • Experience with NLP, LLMs, Generative AI, or Agentic AI. Experience with RAG, embeddings, vector databases, prompt engineering, or AI agents. Experience with cloud AI/ML platforms such as GCP Vertex AI, AWS, or Azure. Experience with ML pipelines, APIs, data pipelines, and model deployment. Knowledge of MLOps, model monitoring, and CI/CD for ML applications. Experience with SQL and large-scale data processing. Exposure to AI/ML experimentation, benchmarking, and model optimization