Generative AI Platform Architect

7 hours ago

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GPON VDSL2 ADSL2+ Metro-Ethernet VoIP IPTV FTTH MSAP MSAN H.248 GR-303 10GE

1001 - 5000

💰 $50M Venture Round on 2009-08

Description

• Design and lead the implementation of scalable, robust AI platforms that support the deployment of Generative AI models (e.g., GPT, BERT, GANs, VAEs). • Conduct and guide research into the latest advancements in Generative AI. • Oversee the development of AI applications from concept to production. • Develop highly scalable AI platforms that can efficiently handle large-scale datasets and AI workloads. • Work closely with data scientists, ML engineers, software developers, and business stakeholders. • Lead the prototyping and experimentation with new generative models. • Implement best practices for AI governance, ensuring adherence to security, privacy, and ethical standards. • Provide mentorship to junior AI architects, data scientists, and engineers.

Requirements

• Master’s or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or related fields. • 10+ years of experience in AI/ML roles, with at least 3-4 years focused on designing AI architectures and deploying large-scale AI platforms. • Extensive experience with Generative AI models (e.g., GPT, BERT, GANs, VAEs) and deep learning architectures. • Proven track record of developing end-to-end AI solutions and deploying them at scale, with strong proficiency in both research and production environments. • Expertise in deep learning frameworks (e.g., TensorFlow, PyTorch) and hands-on experience with building, training, and deploying large-scale models. • Deep understanding of cloud platforms (AWS, GCP, Azure) and experience in architecting AI/ML infrastructure on cloud services. • Experience with data engineering, including handling large datasets, creating data pipelines, and ensuring data quality for training AI models. • Proficiency in MLOps practices and tools for deploying and maintaining AI models in production environments (e.g., Kubernetes, Docker, Kubeflow, MLflow). • Knowledge of distributed systems and high-performance computing for scaling large AI models. • Strong leadership and communication skills, with the ability to work across technical and non-technical teams. • Exceptional problem-solving skills, creativity, and a passion for research and innovation. • Ability to translate complex technical concepts into practical, scalable business solutions.