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