Machine Learning Engineer

Designation: - ML / MLOPs Engineer Location: - Noida (Sector- 132) Key Responsibilities: • Model Development & Algorithm Optimization : Design, implement, and optimize ML models and algorithms using libraries and frameworks such as TensorFlow , PyTorch , and scikit-learn to solve complex business problems. • Training & Evaluation : Train and evaluate models using historical data, ensuring accuracy, scalability, and efficiency while fine-tuning hyperparameters. • Data Preprocessing & Cleaning : Clean, preprocess, and transform raw data into a suitable format for model training and evaluation, applying industry best practices to ensure data quality. • Feature Engineering : Conduct feature engineering to extract meaningful features from data that enhance model performance and improve predictive capabilities. • Model Deployment & Pipelines : Build end-to-end pipelines and workflows for deploying machine learning models into production environments, leveraging Azure Machine Learning and containerization technologies like Docker and Kubernetes . • Production Deployment : Develop and deploy machine learning models to production environments, ensuring scalability and reliability using tools such as Azure Kubernetes Service (AKS) . • End-to-End ML Lifecycle Automation : Automate the end-to-end machine learning lifecycle, including data ingestion, model training, deployment, and monitoring, ensuring seamless operations and faster model iteration. • Performance Optimization : Monitor and improve inference speed and latency to meet real- time processing requirements, ensuring efficient and scalable solutions. • NLP, CV, GenAI Programming : Work on machine learning projects involving Natural Language Processing (NLP) , Computer Vision (CV) , and Generative AI (GenAI) , applying state-of-the-art techniques and frameworks to improve model performance. • Collaboration & CI/CD Integration : Collaborate with data scientists and engineers to integrate ML models into production workflows, building and maintaining continuous integration/continuous deployment (CI/CD) pipelines using tools like Azure DevOps , Git , and Jenkins . • Monitoring & Optimization : Continuously monitor the performance of deployed models, adjusting parameters and optimizing algorithms to improve accuracy and efficiency. • Security & Compliance : Ensure all machine learning models and processes adhere to industry security standards and compliance protocols , such as GDPR and HIPAA . • Documentation & Reporting : Document machine learning processes, models, and results to ensure reproducibility and effective communication with stakeholders. Required Qualifications: • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. • 3+ years of experience in machine learning operations (MLOps), cloud engineering, or similar roles. • Proficiency in Python , with hands-on experience using libraries such as TensorFlow , PyTorch , scikit-learn , Pandas , and NumPy . • Strong experience with Azure Machine Learning services, including Azure ML Studio , Azure Databricks , and Azure Kubernetes Service (AKS) . • Knowledge and experience in building end-to-end ML pipelines, deploying models, and automating the machine learning lifecycle. • Expertise in Docker , Kubernetes , and container orchestration for deploying machine learning models at scale. • Experience in data engineering practices and familiarity with cloud storage solutions like Azure Blob Storage and Azure Data Lake . • Strong understanding of NLP , CV , or GenAI programming, along with the ability to apply these techniques to real-world business problems. • Experience with Git , Azure DevOps , or similar tools to manage version control and CI/CD pipelines. • Solid experience in machine learning algorithms , model training , evaluation , and hyperparameter tuning