About the role
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.