KEY RESPONSIBILITIES
Work with large and complex data sets to solve challenging business problems
Efficient training and deployment of standard ML, NN, and Agentic models.
Develop, train, and optimize machine learning models using state-of-the-art algorithms and frameworks
Build production grade end-to-end ML pipelines, including data ingestion, transformation, model training, validation, and deployment
Automate workflows for model training, testing, and deployment using CI/CD pipelines and MLOps tools
Collaborate with cross-functional teams to integrate models into applications and deliver end-to-end solutions
Finetune SLMs/LLMs and build complex AI architectures
Desired Candidate Profile
(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 Education qualifications: Master or PhD in Computer Science or a related field.
Skills
Machine Learning
AI-driven solutions
ML system architecture