Title: AI Enterprise Architect
Location: US, remote
Employment: W2
Length: up to 1 year
Must have:
- Data Science
- GenAI Tech Stack
- LLMOps
- MLOps
- Python
JD:
Experience in leading and delivering enterprise AI
platform architectural thinking, and its practical
application.
• Experience in the use of conceptual and logical data
modelling technologies.
• Experience in defining and working with information and
data regulatory governances.
• Experience in designing modular, scalable AI
architectures for NLP and agent-based systems
• Experience in AI engineering such as MLOps, LLMOps,
containerization, orchestration, etc.
• Experience in known industry IT architectural patterns
and IT architecture ways of working/methodologies (e.g.
Amazon Bedrock, Amazon Quick Suite and Sagemaker).
• Understands AI Platforms concepts and cloud-based
containerization strategies for hybrid cloud
environments.
• Experience in the appropriate AI structure and
technology based on business use case and completely
familiar with AI lifecycles.
• Hands on experience coding in Python, IaC, etc
Enterprise AI Architecture Accountabilities:
• Collaborate with data scientists and other AI professionals to augment digital transformation efforts by identifying
and piloting use cases. Discuss the feasibility of use cases along with architectural design with business teams and
translate the vision of business leaders into realistic technical implementation. At the same time, bring attention to
misaligned initiatives and impractical use cases.
• Align technical implementation with existing and future requirements by gathering inputs from multiple
stakeholders — business users, data scientists, security professionals, data engineers and analysts, and those in IT
operations — and developing processes and products based on the inputs.
• Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source
and commercial offerings. Select cloud, on-premises, or hybrid deployment models, and ensure new tools are wellintegrated with existing data management and analytics tools.
• Audit AI tools and practices across data, models, and software engineering with a focus on continuous
improvement. Ensure a feedback mechanism to assess AI services, support model recalibration and retrain
models.
• Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model
theft and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. Remain
acquainted with upcoming regulations and map them to best practices.
• AI architecture and pipeline planning. Understand the workflow and pipeline architectures of ML and deep
learning workloads. An in-depth knowledge of components and architectural trade-offs involved across the data
management, governance, model building, deployment and production workflows of AI is a must.
• Software engineering and DevOps principles, including knowledge of DevOps workflows and tools, such as Git,
containers, Kubernetes and CI/CD.
• Data science and advanced analytics, including knowledge of advanced analytics tools (such as SAS, R and Python)
along with applied mathematics, ML and Deep Learning frameworks (such as TensorFlow) and ML techniques (such
as random forest and neural networks).
• Thought leadership. Be change agents to help the organization adopt an AI-driven mindset. Take a pragmatic
approach to the limitations and risks of AI, and project a realistic picture in front of IT executives who provide
overall digital thought leadership.
• Collaborative mindset. To ensure that AI platforms deliver both business and technical requirements, seek to
collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business
unit leaders and CxOs (technical and nontechnical personnel), and harmonize the relationships among them.
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