AI/ML Engineer

Job description

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. **Roles & 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. **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). **Additional Information:** - 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.