Machine Learning Engineer – TCP Corps
Job Description
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)
