Overview

As an AI / ML Engineer, you will work across the complete ML lifecycle from data preparation and model development to deployment, monitoring, and optimization.

Responsibilities

• As an AI / ML Engineer, you will work across the complete ML lifecycle from data preparation and model development to deployment, monitoring, and optimization. You will collaborate with data scientists, software engineers, and business teams to convert complex business problems into scalable AI/ML solutions.
• Design, develop, and deploy Machine Learning and AI solutions for enterprise use cases.
• Build and optimize ML models for classification, regression, forecasting, recommendation, NLP, computer vision, or other business applications.
• Perform data preprocessing, feature engineering, model training, validation, and evaluation.
• Develop production-ready Python-based ML applications and APIs.
• Implement scalable ML pipelines for training, deployment, and inference.
• Integrate ML models with existing applications and enterprise systems.
• Work with large and complex datasets to identify patterns and generate actionable insights.
• Optimize model performance, scalability, latency, and reliability.
• Deploy models using Docker and Kubernetes in cloud environments.
• Implement MLOps practices including model versioning, CI/CD, monitoring, and automated deployment.
• Collaborate with Data Scientists, Software Engineers, Data Engineers, and DevOps teams.
• Conduct experimentation and continuously evaluate new ML techniques, frameworks, and tools.
• Troubleshoot production ML issues and improve model reliability.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field.
• 3+ years of relevant experience in AI/ML engineering or software engineering with substantial ML exposure.
• Robust analytical and problem-solving skills.