Machine Learning Engineer II

3 weeks ago

Hyderabad, India Microsoft Full time
Overview

Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world.The AI Platform organization at Microsoft builds the end-to-end Azure AI stack/PaaS and is core to Azure’s innovation and differentiation, as well as all of Microsoft’s flagship products, from Office to Teams, to Xbox. We are the team building Azure OpenAI, Azure ML, Cognitive Services, and the global Azure AI infrastructure for running the largest AI workloads on the planet.We do not just value differences or different perspectives. We seek them out and invite them in so we can tap into the collective power of everyone in the company. As a result, our customers are better served.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.#aiplatform

Qualifications

Required Qualifications:5+ years of experience writing production quality codeGood understanding in machine learning, deep learning, natural language processing, generative AI and multimodal learning.Experience with transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion).Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) to develop production-grade quality product.Ability to debug, read code and work on a large and increasing codebase.Proficiency in Agile development practices and Continuous Integration/Continuous Deployment (CI/CD)Good 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.Preferred/Additional Qualifications:Preferred training & fine-tuning experience on large dataPreferred experience with distributed systems design and implementation#AIPLATFORM #IDCAIPlatformHiring #IDCAMLAOH

Responsibilities

As a Machine Learning Engineer in our team, you will:Collaborate with researchers, data scientists and software engineers to tackle challenging problems in large scale training and fine-tuning, performance and scalability of LLMs, LVMs for various tasks such as natural language, text analytics, computer vision, conversational AI, and machine translation using state-of-the-art models.Conduct experiments to evaluate model performance, robustness, and generalization.Stay up to date with the latest advancements in NLP, deep learning, and AI research.Work with large-scale datasets - preprocessing them, and creating appropriate data representations, ensuring 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.Qualifications:Good understanding in machine learning, deep learning, natural language processing, generative AI and multimodal learning.Experience with transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion).Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) ) to develop production-grade quality product.Ability to debug, read code and work on a large and increasing codebase.Proficiency in Agile development practices and Continuous Integration/Continuous Deployment (CI/CD)Good 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.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

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