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

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