AI/ML Support Engineer
Full time @AI Jobs Philippines posted 4 weeks ago in Information Technology and Services Shortlist Email JobJob Detail
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Job ID 992
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Experience Less Than 1 Year
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Industry AI Software Development
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Qualifications Degree Bachelor
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Job Location Type Office
Job Description
Who we are:
Accenture in the Philippines is a pioneer in Accenture’s global delivery network. Over the past 30 years, we have expanded our capabilities to become a powerhouse company providing end-to-end technology and business services. As part of Accenture’s global footprint in over 120 countries, covering 40-plus industries, we have been working with the biggest companies in the country and around the globe.
Innovation, a constant at Accenture, enables us to find new ways to stay ahead of our clients’ challenges. Our inclusive, diverse, and strong culture of equality helps us constantly drive innovation in the workplace. By combining our industry expertise and the deep skills of our people with the latest technologies and our uncompromising high-performance standards, we help organizations grow their business and succeed in the digital age.
Job Responsibilities
Ideal candidates should possess a strong foundation in AIML, machine learning, and natural language processing, with a specific emphasis on data analysis, data pipelines, and model deployment. They should be capable of troubleshooting model-related issues, optimizing model performance, and providing expert support for various AIML platforms.
Technical Skills:
● Compute Fundamentals: Demonstrates a deep understanding of compute concepts, including
virtualization, containerization, operating systems, and system administration.
● Cloud Technologies: Experience with cloud platforms like GCP, AWS, and Azure, including their
AI/ML services.
● Generative AI Experience with LLM and Generative AI and Google Cloud Products and services
(e.g Vertex AI, Dialogflow, Gemini)
● ML Development: Experience with Machine Learning model development and deployment.
● AIML Frameworks: Experience with frameworks for deep learning (e.g. PyTorch, Tensorflow, Jax,
Ray, etc.), AI accelerators (e.g. TPUs, GPUs), model architectures (e.g. encoders, decoders,
transformers), and using machine learning APIs.