Machine Learning Engineer

We are seeking a Machine Learning Engineer with strong fundamentals in machine learning and hands-on experience building end-to-end ML systems and pipelines. The ideal candidate should be comfortable working with modern transformer architectures, agentic AI systems, and production-grade ML infrastructure.

Responsibilities:

• Develop and implement machine learning models from research to production.
• Work with transformer-based architectures for NLP and related tasks.
• Design and build agentic AI systems capable of reasoning, planning, and tool usage.
• Build end-to-end ML pipelines, including data processing, model training, evaluation, and deployment.
• Develop and maintain scalable ML systems for production environments.
• Collaborate with engineering and product teams to integrate ML solutions into applications.
• Optimize models and pipelines for performance, reliability, and scalability.
• Monitor model performance and continuously improve deployed systems.

Requirements:

• Experience in ML: 6-12 years.
• Strong understanding of machine learning fundamentals.
• Experience with transformer models and modern deep learning architectures.
• Experience designing agent-based or agentic AI systems.
• Hands-on experience building end-to-end ML pipelines.
• Experience with data preprocessing, model training, evaluation, and deployment workflows.
• Familiarity with ML system design and production ML infrastructure.
• Proficiency in Python and ML frameworks (e. g., PyTorch, TensorFlow, or similar).

Preferred Qualifications:

• Experience with fine-tuning, LLM-based applications, and tool-using agents.
• Experience with distributed training or large-scale ML systems.
• Familiarity with MLOps tools and workflow orchestration (Airflow, Kubeflow, etc. ).
• Experience deploying models using APIs or microservices.