AI/ML/GEN AI Engineer

About the Role

• Design, develop, and deploy machine learning models using Python and popular ML frameworks.
• Implement NLP solutions using NLP techniques such as 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.
• Perform data collection, profiling, exploration data analysis (EDA), and data preparation.
• Apply a range of ML techniques including supervised, unsupervised, and reinforcement learning.

Key Responsibilities

• Strong proficiency in Python for data processing, automation, and model development.
• Deep understanding of ML model lifecycle: training, evaluation, and deployment.
• Solid knowledge of cloud-based AI services (Azure, AWS, GCP).
• 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.

Requirements & Tech Stack

• Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with Python-based web applications for AI/ML use cases.

Qualifications

• 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 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.
• Familiarity with front-end technologies like Angular or React.js is a valuable addition.