Jobs in Gran Bretaña

As a hiring partner for many IT organizations, We are hiring for below position as direct full time on the payroll as a permanent employee of the Hiring Organization. Please share your updated word format resume with CTC, Location and Notice period at "info@unimorphtech.com"

Machine learning engineer Sr. manager

Experience : 8-12+ Yrs

Location : Bangalore or Chennai

Python, Python libraries like NumPy, Pandas, PyTorch, TensorFlow, and Scikit-learn

Design & deploy Machine learning pipelines on cloud platform(Azure), MLOPs, MLOps tools such as MLFlow and Weights & Biases, ML deployments, Machine learning frameworks & GRC.

ETL pipeline for data processing on Azure, Snowflake, CI-CD using tools like Git and Jenkins.

We are looking for a highly skilled and motivated Machine Learning Engineer to join our team to design, develop and deploy scalable machine learning solutions. In this role, you will work on building robust ML pipelines, optimizing large-scale data processing and implementing state-of-the-art MLOps frameworks on cloud platforms like Azure and Snowflake.

• Design and deploy end-to-end machine learning pipelines on cloud platforms (Azure, Snowflake).
• Build efficient ETL pipelines to support data preparation, model training and evaluation on Snowflake and Azure.
• Scale machine learning infrastructure to handle large datasets.
• Ensure secure ML deployments with Governance, Risk and Compliance.
• Experience building scalable ML systems.

This is a global role working across diverse business areas, brand and geographies, providing business outcomes and enabling transformative impact across the global landscape.

• Design and deploy end-to-end machine learning pipelines on cloud platforms (Azure, Snowflake) to deliver scalable, production-ready solutions.
• Build efficient ETL pipelines to support data preparation, model training and evaluation on modern platforms like Snowflake.
• Scale machine learning infrastructure to handle large datasets and enable real-time processing for critical applications.
• Implement MLOps frameworks to automate model deployment, monitoring and retraining, ensuring seamless integration of ML solutions into business workflows.
• Monitor and measure model drift (concept, data and performance drift) to maintain ongoing model effectiveness.
• Deploy machine learning models as REST APIs using frameworks such as FastAPI, Bento ML or TorchServe.
• Establish robust CI/CD pipelines for machine learning workflows using tools like Git and Jenkins, enabling efficient and repeatable deployments.
• Ensure secure ML deployments, addressing risks such as adversarial attacks and maintaining model integrity.
• Build modular and reusable ML packages using object-oriented programming principles, promoting code reusability and efficiency.
• Develop clean, efficient and production-ready code by translating complex business logic into software solutions.
• Continuously explore and evaluate MLOps tools such as MLFlow and Weights & Biases, integrating best practices into the development process.
• Foster cross-functional collaboration by partnering with product teams, data engineers and other stakeholders to align ML solutions with business objectives.
• Lead data labeling and preprocessing tasks to prepare high-quality datasets for training and evaluation.
• Stay updated on advancements in machine learning, cloud platforms and secure deployment strategies to drive innovation in ML infrastructure.

• Masters degree in Computer Science, Computational Sciences, Data Science, Machine Learning, Statistics, Mathematics or any quantitative field.
• Expertise with object-oriented programming (Python, C++).
• Strong expertise in Python libraries like NumPy, Pandas, PyTorch, TensorFlow and Scikit-learn.
• Proven experience in designing and deploying ML systems on cloud platforms (AWS, GCP or Azure).
• Hands‑on experience with MLOps frameworks, model deployment pipelines and model monitoring tools.
• Track record of scaling machine learning solutions from prototype to production.
• Experience building scalable ML systems in fast-paced, collaborative environments.
• Working knowledge of adversarial machine learning techniques and their mitigation.
• Agile and Waterfall methodologies.
• Personally invested in continuous improvement and innovation.
• Motivated, self‑directed individual who works well with minimal supervision.