On-Site
Full-Time
Noida, Uttar Pradesh
India
About the Role
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:
• Bachelor’s or Master’s 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
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:
• Bachelor’s or Master’s 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