Principal Machine Learning Engineer - Hyderabad, India - Microsoft

Description

Overview

Within AI Platform, the Azure ML team enables data scientists and developers to quickly and easily build, train, deploy, manage, and consume machine learning models.

At Microsoft, we're passionate about pushing the boundaries of generative artificial intelligence (AI) and natural language understanding.

Our Azure Machine Learning Training and Finetuning Team is at the forefront of this mission, working on groundbreaking projects that shape the future of AI-driven applications.

We collaborate closely with research institutions, industry leaders, and organizations worldwide to create innovative solutions that impact millions of users.

Qualifications

Depth in Data Science, Generative AI and EngineeringA strong background in machine learning, deep learning, and natural language processing.

Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch).Experience with transformer-based models (e.g., BERT, GPT, T5, Llama).Solid understanding of statistics, linear algebra, and probability theory.

Familiarity with cloud platforms (e.g., Azure, AWS) and distributed computing.

Excellent problem-solving skills and the ability to work independently and collaboratively.#aiplatform, #machinelearning #openai #finetuning #training #llm #multimodal #transformers #chatgptAbility to meet Microsoft, customer and/or government security screening requirements are required for this role.

These requirements include but are not limited to the following specialized security screenings:

Microsoft Cloud Background Check:

This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

#exploreidc

Responsibilities

As a (Principal)

Machine Learning Engineer in our team, you will:
Collaborate with researchers and data scientists to design sophisticated machine learning models.
Implement and fine-tune neural network architectures, including transformer-based models.
Optimize model performance, scalability, and efficiency.
Conduct experiments to evaluate model performance, robustness, and generalization.
Explore novel techniques and approaches to enhance model capabilities.
Stay up-to-date with the latest advancements in NLP, deep learning, and AI research.
Work with large-scale datasets, preprocess them, and create appropriate data representations.
Select relevant features and ensure data quality for training and evaluation.
Collaborate with cross-functional teams, including researchers, software engineers, and product managers.
Communicate technical findings and insights effectively.
Deploy trained models in production environments.
Monitor model performance, troubleshoot issues, and iterate on improvements.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

Industry leading healthcareEducational resourcesDiscounts on products and servicesSavings and investmentsMaternity and paternity leaveGenerous time awayGiving programsOpportunities to network and connect