Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Machine Learning
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary: As an AI / ML Engineer, you will engage in the development of applications and systems that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready solutions, ensuring that they meet quality standards. You will work with various AI models, including generative AI, deep learning, and neural networks, while also exploring innovative applications such as chatbots and image processing. Collaboration with cross-functional teams will be essential to integrate these solutions effectively into existing systems and pipelines. Roles & Responsibilities: - Expected to be an SME. - Collaborate and manage the team to perform. - Responsible for team decisions. - Engage with multiple teams and contribute on key decisions. - Provide solutions to problems for their immediate team and across multiple teams. - Mentor junior team members to enhance their skills and knowledge. - Continuously evaluate and improve existing AI models and applications. Professional & Technical Skills: - Azure Machine Learning Services: - worked on Azure's ML services like Azure ML Studio for designing, deploying, and improving machine learning models is essential. - Understanding various Azure resources (such as Azure Databricks, Azure Machine Learning pipelines, and Azure Cognitive Services) and how they integrate is vital. - Programming Skills: Proficiency in programming languages such as Python (R is a plus), PowerShell/AzureCli are essential, as these are commonly used for data science and machine learning tasks on Azure. Data Engineering: - Understanding of data manipulation/wrangling techniques using Spark (SQL/Scala/Python ) or Azure Data Factory - familiarity with big data technologies hosted on Azure like HDInsight/ Cosmos DB / Azure SQL Data Warehouse. - Ability to handle large datasets and understanding data streaming processes. - Machine Learning, Deep Learning, GENAI (Good to Have): - Understanding of machine learning algorithms (supervised and unsupervised learning, neural networks, TensorFlow or PyTorch etc.) concepts and their applications. - Understanding of Agentic framework extensive experience with the fine tuning of LLM across the different type of model like LLAMA, OPEN AI, QWEN, etc Additional Information: - The candidate should have minimum 5 years of experience in Machine Learning. - A 15 years full time education is required.

15 years full time education

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

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