MLOps Engineer

Designation : ML / MLOPs Engineer

Location : Noida (Sector- 132)

Key Responsibilities :

- 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