Model Development & Algorithm Optimization : Design, implement, and optimize ML models and algorithms using libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn to 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, leveraging Azure Machine Learning and containerization technologies like Docker and Kubernetes.
Production Deployment : Develop and deploy machine learning models to production environments, ensuring scalability and reliability using tools such as Azure 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 improve inference speed and latency to meet real-time processing requirements, ensuring efficient and scalable solutions.
NLP, CV, GenAI Programming : Work on machine learning projects involving Natural Language Processing (NLP), Computer Vision (CV), and Generative 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 like Azure DevOps, Git, and Jenkins.
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 industry security standards and compliance protocols, such as GDPR and HIPAA.
Documentation & Reporting : Document machine learning processes, models, and results to ensure reproducibility and effective communication with stakeholders.
Required Qualifications
Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.
3+ years of experience in machine learning operations (MLOps), cloud engineering, or similar roles.
Proficiency in Python, with hands-on experience using libraries such as TensorFlow, PyTorch, scikit-learn, Pandas, and NumPy.
Strong experience with Azure Machine Learning services, including Azure ML Studio, Azure Databricks, and Azure Kubernetes Service (AKS).
Knowledge and experience in building end-to-end ML pipelines, deploying models, and automating the machine learning lifecycle.
Expertise in Docker, Kubernetes, and container orchestration for deploying machine learning models at scale.
Experience in data engineering practices and familiarity with cloud storage solutions like Azure Blob Storage and Azure Data Lake.
Strong understanding of NLP, CV, or GenAI programming, along with the ability to apply these techniques to real-world business problems.
Experience with Git, Azure DevOps, or similar tools to manage version control and CI/CD pipelines.
Solid experience in machine learning algorithms, model training, evaluation, and hyperparameter tuning
