Machine Learning Engineer

Designation: -ML / MLOPs Engineer Location: -Noida (Sector- 132) Key Responsibilities: •Model Development & Algorithm Optimization : Design, implement, and optimizeML models and algorithmsusing libraries and frameworks such asTensorFlow ,PyTorch , and scikit-learnto 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, leveragingAzure Machine Learningand containerization technologies likeDockerandKubernetes . •Production Deployment : Develop and deploy machine learning models to production environments, ensuring scalability and reliability using tools such asAzure 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 improveinference speedandlatencyto meet real- time processing requirements, ensuring efficient and scalable solutions. •NLP, CV, GenAI Programming : Work on machine learning projects involvingNatural Language Processing (NLP) ,Computer Vision (CV) , andGenerative 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 likeAzure DevOps ,Git , andJenkins . •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 industrysecurity standardsandcompliance protocols , such asGDPRandHIPAA . •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+ yearsof experience in machine learning operations (MLOps), cloud engineering, or similar roles. • Proficiency inPython , with hands-on experience using libraries such asTensorFlow , PyTorch ,scikit-learn ,Pandas , andNumPy . • Strong experience withAzure Machine Learningservices, includingAzure ML Studio , Azure Databricks , andAzure Kubernetes Service (AKS) . • Knowledge and experience in building end-to-end ML pipelines, deploying models, and automating the machine learning lifecycle. • Expertise inDocker ,Kubernetes , andcontainer orchestrationfor deploying machine learning models at scale. • Experience indata engineeringpractices and familiarity with cloud storage solutions like Azure Blob StorageandAzure Data Lake . • Strong understanding ofNLP ,CV , orGenAIprogramming, along with the ability to apply these techniques to real-world business problems. • Experience withGit ,Azure DevOps , or similar tools to manage version control and CI/CD pipelines. • Solid experience inmachine learning algorithms ,model training ,evaluation , and hyperparameter tuning