GEN AI Engineer
Infosys
Bengaluru East, Karnataka, India
- 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).
- 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.