Zensar Technologies - Machine Learning Engineer - Generative AI

Machine Learning Engineer

Location : Hyd or NCR

Experience : 7+ Years

Employment Type : Full-time

Job Overview :

We are seeking a skilled and passionate Machine Learning Engineer with 3-5 years of hands-on experience. The ideal candidate will design, develop, and deploy cutting-edge machine learning solutions, with a strong focus on modern Generative AI (GenAI) techniques. You will work at the intersection of traditional ML engineering and emerging generative AI technologies, building scalable systems that leverage both conventional algorithms and state-of-the-art language models.

Key Responsibilities :

• ML Solution Design & Development : Design, develop, and implement robust and scalable machine learning solutions, from data ingestion and feature engineering to model deployment.
• Generative AI Focus : Specifically apply and develop expertise in modern Generative AI techniques, including working with large language models (LLMs) and other generative models.
• Model Training & Evaluation : Train, fine-tune, and evaluate machine learning models, including deep learning architectures, ensuring optimal performance and accuracy.
• System Integration : Integrate ML models and GenAI solutions into existing production systems and applications.
• Data Pipeline Development : Build and maintain efficient data pipelines to support ML model training and inference.
• Code Quality & MLOps : Write clean, maintainable, and well-documented code. Contribute to MLOps practices for model versioning, deployment, monitoring, and retraining.
• Research & Innovation : Stay up-to-date with the latest advancements in machine learning, deep learning, and generative AI, actively exploring new techniques and tools.
• Collaboration : Collaborate closely with data scientists, software engineers, and product managers to understand business requirements and translate them into technical ML solutions.
• Troubleshooting & Optimization : Identify and resolve performance bottlenecks in ML pipelines and models, ensuring efficient resource utilization.

Requirements :

(ref : hirist.tech)