Machine Learning Engineer

Designation: - ML / MLOPs EngineerLocation: - Noida (Sector- 132)Key Responsibilities:• Model Development & Algorithm Optimization: Design, implement, and optimize MLmodels and algorithms using libraries and frameworks such as TensorFlow, PyTorch, andscikit-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 suitableformat for model training and evaluation, applying industry best practices to ensure dataquality.• Feature Engineering: Conduct feature engineering to extract meaningful features from datathat enhance model performance and improve predictive capabilities.• Model Deployment & Pipelines: Build end-to-end pipelines and workflows for deployingmachine learning models into production environments, leveraging Azure MachineLearning and containerization technologies like Docker and Kubernetes.• Production Deployment: Develop and deploy machine learning models to productionenvironments, ensuring scalability and reliability using tools such as Azure KubernetesService (AKS).• End-to-End ML Lifecycle Automation: Automate the end-to-end machine learninglifecycle, including data ingestion, model training, deployment, and monitoring, ensuringseamless 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 NaturalLanguage 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 tointegrate ML models into production workflows, building and maintaining continuousintegration/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 toindustry security standards and compliance protocols, such as GDPR and HIPAA.• Documentation & Reporting: Document machine learning processes, models, and results toensure reproducibility and effective communication with stakeholders.Required Qualifications:• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a relatedfield.• 3+ years of experience in machine learning operations (MLOps), cloud engineering, orsimilar 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, andautomating the machine learning lifecycle.• Expertise in Docker, Kubernetes, and container orchestration for deploying machinelearning models at scale.• Experience in data engineering practices and familiarity with cloud storage solutions likeAzure Blob Storage and Azure Data Lake.• Strong understanding of NLP, CV, or GenAI programming, along with the ability to applythese techniques to real-world business problems.• Experience with Git, Azure DevOps, or similar tools to manage version control and CI/CDpipelines.• Solid experience in machine learning algorithms, model training, evaluation, andhyperparameter tuning