Machine Learning Engineer

Responsibilities

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)

Skills: time series forecasting,bitbucket,apache airflow,kserve,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,decision trees,torchserve,azure synapse analytics,linear regression,k-means clustering,azure machine learning,tensorflow,aws code commit,github,hdfs,pytorch,xgboost,scikit-learn,apache spark,mongodb,sql,azure data factory,snowflake,aws kinesis,aws,hadoop,mlflow,java,google bigquery,kubernetes,presto,hypothesis testing,gitlab,aws s3,tensorflow serving,pandas,git,aws rds,scala,azure devops,machine learning,python,keras,random forests,support vector machines,restful apis