Machine Learning Engineer

Designs and develops scalable machine learning models and AI-driven solutions to address complex business challenges and enhance decision-making processes 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 Employment Type Full-time