Machine Learning Engineer

ROLE PROFILE

• 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

Professional Experience/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 Education qualifications: Master or PhD in Computer Science or a related field.