Machine Learning 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:

• 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