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). • Hands-on experience with Natural Language Processing (NLP) techniques and tools. • Experience in developing Generative AI solutions, including: • Retrieval-Augmented Generation (RAG) pipelines for enhanced contextual responses. • Agent-based AI systems leveraging Large Language Models (LLMs) for autonomous task execution and decision-making • 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->Generative AI - Basic,Technology->AI-Generative AI->Prompt Engineering,Technology->Programming Languages - OS->Python - Open Source->Python