What are the responsibilities and job description for the Google Cloud Platform AIML Lead Architect position at Nityo Infotech Corporation?
Job Details
Please share profiles as per must have :
- Google Cloud Platform Stack Knowledge
- Vertex AI
- Google Cloud Platform Analytics
- Chatbot Coding Experience
- Prompt Engineering Experience
- Dialog flow
- React JS (will be good not mandatory)
JD :
Design and architect scalable AI platforms to develop, deploy AI solutions leveraging ML techniques and Deep Learning Techniques.
Drive Joint Architecture Design to collaborating with business stakeholders, data scientists, engineering teams, product, and other key partners to gather functional, non-functional requirements for solving AI use case on the AI Platform
Evaluate emerging technologies and tools in AI area and do fitment analysis to the Enterprise AI Platform and capabilities strategy.
Define and implement AI/ML architecture best practices, frameworks, and standards.
Lead AI/ML infrastructure setup, including cloud services selection, data pipelines, and model deployment.
Ensure robustness, reliability, and scalability of AI/ML solutions in production environments.
Design and implement data governance, security, and compliance measures for AI/ML platforms.
Optimize AI/ML workflows for performance, cost efficiency, and resource utilization.
Provide technical leadership and mentorship to AI/ML development teams.
Communicate AI/ML architecture decisions and strategies to stakeholders and executives.
Proven experience as an AI/ML platform architect
Deep understanding of ML algorithms, Deep Learning architecture, models, and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
Expertise in cloud platforms Google Cloud Platform) and their AI services.
Strong knowledge of Model development life cycle, software engineering principles, data engineering principlesExperience with containerization and orchestration tools onprem and cloud (e.g., AKS, GKE, OpenShift Container Platform, Docker, Kubernetes) for deploying AI/ML models.
Ability to design and optimize distributed computing systems for AI/ML workloads.
Familiarity with DevOps practices, CI/CD pipelines, and automation tools in AI-ML contexts.
Excellent problem-solving skills and ability to address complex technical challenges.
Effective communication skills to collaborate with cross-functional teams and stakeholders
