Machine Learning Engineer
14 hours ago
• Develop, deploy, and maintain machine learning models using AWS Sagemaker and MLFlow.
• Implement end-to-end ML pipelines, from data ingestion to model deployment.
• Optimize model performance and scalability.
• Collaborate with data scientists to transition models from development to production.
• Implement Data Science Techniques (Statistical Analysis, Hypothesis Testing, Time Series Forecasting, Natural Language Processing, Classical Machine Learning Algorithms such as Linear Regression, Decision Trees, Random Forests, Support Vector Machines, K-Means Clustering)
• Monitor and manage deployed models to ensure performance and accuracy.
• Document processes and workflows to support reproducibility and transparency.
• Stay updated with the latest advancements in machine learning and related technologies.
• Design and implement data preprocessing and feature engineering pipelines.
• Integrate machine learning models with existing data systems and applications.
• Perform hyperparameter tuning and model validation to ensure high performance.
• Implement continuous integration and continuous deployment (CI/CD) pipelines for ML models.
• Conduct regular audits and performance checks of ML models in production.
• Provide technical guidance and mentorship to junior ML engineers and data scientists. Technical Expertise Expected:
• Machine Learning Frameworks (TensorFlow, PyTorch & Scikit-learn, Keras, XGBoost, LightGBM, CatBoost)
• MLOps Tools (AWS Sagemaker, MLFlow, Kubeflow, Docker, Kubernetes, Azure Machine Learning, Google AI Platform, Jenkins and AWS/Azure/GCP CI/CD techniques)
• Programming Languages (Python, SQL, Scala, Java, R)
• ML Pipeline integration with Data Processing and Querying tech stack (Apache Spark, Apache Airflow, Apache Kafka, AWS Kinesis, Azure Data Factory, Pandas, Presto)
• Model Deployment (RESTful APIs, Flask, FastAPI, Sagemaker Deploy, AWS Lambda, Azure Functions, Tensorflow Serving, TorchServe, Kubernetes with KServe)
• Version Control and Collaboration (Git, GitHub, GitLab, Bitbucket, AWS Code Commit, Azure DevOps)
• Big Data Technologies (Hadoop, HDFS)
• ML Ops Framework Integration with Data Storage Solutions (Amazon Redshift, Snowflake, Google BigQuery, Azure Synapse Analytics,AWS RDS, AWS S3, Azure Blob Storage, Google Cloud Storage, MongoDB, Cassandra) Qualifications:
• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Engineering, or a related field.
• Proven experience as an ML Engineer with hands-on experience in Sagemaker and MLFlow (Minimum 4 years).
• Strong programming skills in Python and proficiency in ML libraries such as TensorFlow, PyTorch, and scikit-learn.
• Experience with on-premise and cloud-based ML solutions and architectures.
• Hands-on experience with ML Ops framework, practices and tools.
• Excellent problem-solving skills and ability to work independently.
• Strong communication and teamwork skills. Preferred Certifications:
• AWS Certifications (AWS Certified Machine Learning – Specialty, AWS Certified Solutions Architect – Associate/Professional)
• Azure Certifications (Microsoft Certified: Azure Data Scientist Associate)
• Google Cloud Certifications (Google Professional Machine Learning Engineer)
• MLOps and Data Engineering Certifications (Databricks Certified Machine Learning Professional)
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