AI/ML/GEN AI Engineer
Strong proficiency in Python for data processing, automation, and model development. Deep understanding of ML model lifecycle: training, evaluation, and deployment. Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Good to have experience integrating GenAI capabilities into enterprise applications using platforms like Microsoft Copilot Studio. Good to have experience in monitoring model performance and conduct thorough evaluations using metrics such as Precision, Recall, F1 Score, and BLEU Understanding of Responsible AI practices including model fairness, transparency, and auditability. Hands-on experience with Python-based web applications for AI/ML use cases. Solid knowledge of cloud-based AI services (Azure, AWS, GCP).
Perform data collection, profiling, exploration data analysis (EDA), and data preparation. Apply a range of ML techniques including supervised, unsupervised, and reinforcement learning. Design, develop, and deploy machine learning models using Python and popular ML frameworks Implement NLP solutions using NLP techniques like preprocessing, tokenization, vectorization, and semantic analysis. Develop and deploy GenAI solutions such as RAG systems and Agentic AI. Monitor model performance in production and implement retraining strategies. Adhere to and implement Responsible AI principles in all ML workflows. Present analytical insights to business stakeholders and project teams. Propose ML-based solutions and provide effort estimates for new use cases. Collaborate with data scientists and engineers on model training, evaluation, and deployment. Utilize AI services from cloud platforms such as Azure, AWS, and GCP.
Experience with MLOps frameworks for model lifecycle, versioning, deployment, and monitoring – such as Azure Machine Learning or AWS Sagemaker. Experience with Python-based web frameworks such as Flask and Django is essential, and familiarity with front-end technologies like Angular or React.js is a valuable addition. Hands-on experience in fine-tuning large language models (LLM) using techniques such as LoRA and QLoRA is highly valued. Experience with Kubernetes, docker containerization, and Kafka is preferred. Knowledge on model optimization, model distillation, quantization is an advantage.