What are the responsibilities and job description for the Enterprise Architect AI position at ClifyX?
We are seeking a seasoned AI Architect with 10 years of experience in enterprise architecture, software engineering, and AI/ML solution design. This role demands a strategic thinker and technical leader who can drive AI innovation across the organization, architect scalable solutions, and mentor cross-functional teams. The ideal candidate will have a proven track record of delivering impactful AI systems in complex environments.
Key Responsibilities:
Strategic Leadership
• Define and lead the enterprise-wide AI strategy and architecture roadmap.
• Collaborate with executive leadership to align AI initiatives with business objectives.
• Evaluate emerging AI technologies and guide adoption across business units.
Architecture & Solution Design
• Architect robust, scalable, and secure AI/ML platforms and solutions.
• Lead the design of data pipelines, model training workflows, and deployment frameworks.
• Integrate AI capabilities into existing enterprise systems using APIs and microservices.
Governance & Risk Management
• Establish AI governance frameworks including model lifecycle management, auditability, and ethical AI practices.
• Ensure compliance with global data privacy regulations (GDPR, CCPA, etc.).
Technical Leadership
• Mentor and guide data scientists, ML engineers, and software developers.
• Promote best practices in MLOps, DevOps, and cloud-native AI development.
• Lead technical reviews, architecture boards, and innovation workshops.
Performance & Optimization
• Monitor AI systems in production and optimize for performance, scalability, and cost-efficiency.
• Implement feedback loops and continuous learning systems for model improvement.
Work Environment
• Must work from either client Edison office or customer office for all 5 days
Qualifications:
• Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, or related field.
• 10 years of experience in software architecture, with 5 years focused on AI/ML.
• Deep expertise in Python, TensorFlow, PyTorch, and cloud platforms (AWS, Azure, GCP).
• Strong background in data engineering, distributed systems, and enterprise integration.
• Experience with MLOps tools (MLflow, Kubeflow, Airflow) and containerization (Docker, Kubernetes).
• Experience with generative AI, LLMs, and NLP.
• Certifications in cloud architecture or AI/ML (e.g., AWS Certified Machine Learning, Azure AI Engineer).
• Familiarity with industry-specific AI applications (e.g., manufacturing, healthcare, finance).
• Excellent communication, leadership, and stakeholder management skills.