AI/ML/GEN AI Engineer

We are seeking a dynamic candidate with expertise in Python, Machine Learning, Natural Language Processing (NLP) & GenAI techniques. The ideal candidate should have hands-on experience in designing and implementing end-to-end data science and ML solutions, including model productionisation and guiding development teams on ML use case implementation. A strong background in AI/ML solutioning combined with experience in NLP & GenAI solutions is highly preferred. Candidates with experience in Microsoft Copilot Studio will be a great asset to the team. Technical Requirements: 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). Responsibilities: 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. Preferred Skills: Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->generative ai,Technology->AI-Generative AI->Conversational AI Platform,Technology->AI-Generative AI->Prompt Engineering,Technology->AI-Responsible AI->Responsible AI->explainable ai