• Develop and deploy machine learning models, ensuring scalability and efficiency in production environments.
• Design and implement end-to-end machine learning pipelines, from data ingestion and preprocessing to model evaluation and deployment.
• Conduct rigorous experimentation and A/B testing to optimize model performance and user experience.
• Stay up-to-date with the latest advancements in machine learning and deep learning, and apply them to solve real-world problems.
• Automate model training, evaluation, and deployment processes to streamline workflows.

Desired Candidate Profile

Key Requirements:

• 3–7 years of experience building production-grade, scalable AI systems

• Strong expertise in supervised and unsupervised learning, deep learning, NLP, computer vision, and/or generative AI (LLMs)

• Experience training, fine-tuning, and deploying machine learning, neural network, and Agentic AI models

• Strong machine learning system architecture and design skills

• Experience building end-to-end production-grade ML pipelines covering data ingestion, model training, validation, deployment, and monitoring

• Knowledge of model serving and API development using frameworks such as FastAPI and Flask

• Familiarity with Docker, Kubernetes, CI/CD pipelines, MLflow, Kubeflow, and MLOps practices

• Experience deploying models on AWS and/or Azure

• Bachelor's degree in Computer Science, Engineering, or a related field (Master's or PhD preferred)