Overview

Title: Machine Learning Engineer

Responsibilities

• Design, develop, train, and optimize Machine Learning and Deep Learning models for classification, regression, forecasting, NLP, and computer vision applications.
• Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation.
• Conduct experiments and improve model accuracy, scalability, and reliability.
• Deploy, monitor, and maintain ML models in production environments.
• Build and manage end-to-end ML pipelines using MLOps best practices.
• Implement CI/CD workflows for machine learning applications.
• Containerize applications using Docker and orchestrate deployments with Kubernetes.
• Work with structured and unstructured datasets to build scalable data pipelines.
• Process and analyze large datasets using SQL and distributed data processing tools.
• Collaborate with data engineering teams to ensure high-quality data availability.
• Partner with Data Scientists, Product Managers, Backend Engineers, and Business stakeholders to understand requirements and deliver ML-powered solutions.
• Translate business challenges into scalable machine learning applications.
• Monitor model performance, latency, drift, and reliability in production.
• Continuously improve deployed models through retraining and optimization.
• Implement logging, monitoring, and alerting mechanisms for ML systems.
• Stay updated with the latest advancements in Machine Learning, Deep Learning, Generative AI, and MLOps.
• Evaluate and integrate new frameworks, tools, and best practices into existing workflows.

Qualifications

• 4-6 years of hands-on experience in Machine Learning Engineering, Applied Machine Learning, or related roles.
• Hands-on experience with Large Language Models (LLMs).
• Strong proficiency in Python.
• 4-6 years of hands-on experience in Machine Learning Engineering, Applied Machine Learning, or related roles.
• Strong proficiency in Python.
• Hands-on experience with: TensorFlow, PyTorch, Keras, XGBoost.
• Practical experience with one or more cloud platforms: AWS, Google Cloud Platform (GCP), Microsoft Azure
• Strong knowledge of SQL and NoSQL databases.
• Familiarity with Apache Spark and Hadoop is preferred.
• Hands-on experience with Large Language Models (LLMs).
• Knowledge of Vector Databases such as Pinecone, Weaviate, or Milvus.
• Familiarity with experimentation frameworks, A/B testing, and model evaluation methodologies.

Education

• Bachelor's or Master's degree (B.Tech/M.Tech) in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.