Machine Learning Engineer

Role overview

Responsibilities

• check_circle Work with large and complex data sets to solve challenging business problems
• check_circle Efficient training and deployment of standard ML, NN, and Agentic models.
• check_circle Develop, train, and optimize machine learning models using state-of-the-art algorithms and frameworks
• check_circle Build production grade end-to-end ML pipelines, including data ingestion, transformation, model training, validation, and deployment
• check_circle Automate workflows for model training, testing, and deployment using CI/CD pipelines and MLOps tools
• check_circle Collaborate with cross-functional teams to integrate models into applications and deliver end-to-end solutions
• check_circle Finetune SLMs/LLMs and build complex AI architectures

Basic qualifications

• (3-7) years of experience in building production grade, scalable AI systems.
• Expert in supervised/unsupervised learning, deep learning, NLP, computer vision, or generative AI (e.g., LLMs).
• Strong ML system architecture skills
• Understanding of model serving, API development (FastAPI, Flask), and optimizing model performance for real-time or batch inference.
• General Knowledge of Docker, Kubernetes, CI/CD pipelines, and tools like MLflow/Kubeflow for model lifecycle management (MLOps)
• Comfortable with deploying models on AWS or Azure
• Minimum Educational qualifications: Bachelor’s degree in Computer Science, Engineering, or related field required

Preferred qualifications

• Preferred Education qualifications: Master or PhD in Computer Science or a related field.