Responsibilities:
Develop deploy and maintain machine learning models using AWS Sagemaker and MLFlow.
Implement endtoend 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 KMeans 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 & Scikitlearn 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 AnalyticsAWS 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 handson experience in Sagemaker and MLFlow (Minimum 4 years).
Strong programming skills in Python and proficiency in ML libraries such as TensorFlow PyTorch and scikitlearn.
Experience with onpremise and cloudbased ML solutions and architectures.
Handson experience with ML Ops framework practices and tools.
Excellent problemsolving 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)
time series forecasting,bitbucket,apache airflow,docker,amazon redshift,sagemaker deploy,kubeflow,statistical analysis,google ai platform,aws sagemaker,cassandra,natural language processing,azure functions,jenkins,r,apache kafka,ml,flask,aws lambda,google cloud storage,catboost,fastapi,lightgbm,azure blob storage,torchserve,azure synapse analytics,azure machine learning,models,tensorflow,aws code commit,github,hdfs,pytorch,xgboost,scikit-learn,apache spark,mongodb,sql,azure data factory,snowflake,aws kinesis,data science techniques,aws,hadoop,mlflow,java,google bigquery,kubernetes,presto,hypothesis testing,classical machine learning algorithms,gitlab,aws s3,tensorflow serving,pandas,kubernetes with kserve,git,aws rds,scala,azure devops,machine learning,python,keras,restful apis
Machine Learning Engineer
