Enterprise Architect

tectures that support business goals, ensuring scalability, performance, security, and maintainability.

• Create architectural blueprints and roadmaps that guide the integration of AI and data solutions across the organization.

• Lead the development and implementation of data platforms and AI-driven systems that facilitate advanced analytics, machine learning, and automation.

• Define AI strategy and guide its implementation across business units, ensuring alignment with business objectives.

• Oversee the deployment and integration of AI models, tools, and technologies into production systems.

• Design and implement scalable cloud-based and on-premises data architectures using platforms like Azure, AWS, or Google Cloud.

• Work with big data technologies (e.g., Hadoop, Spark) and data lake architectures to ensure the organization's data can be ingested, processed, and analyzed at scale.

• Manage the integration of AI models and algorithms into big data platforms and ensure the appropriate handling of structured and unstructured data.

• Work closely with business and technical teams to understand business needs and translate them into architectural solutions that leverage AI and data analytics.

• Stay current with the latest advancements in AI, machine learning, data technologies, and architecture best practices.

• Lead the adoption and implementation of emerging AI technologies, ensuring the organization remains competitive and innovative.

• Assess and mitigate risks associated with data and AI architectures, ensuring secure handling of sensitive and confidential data.

Qualifications:

• Bachelor's with 12+ years of relevant experience or Master's degree with 10+ years of relevant experience in Computer Science, Information Systems, Data Science, Engineering, or a related field.

• 7+ years of experience in enterprise architecture, with at least 3-5 years of focused experience in AI and data-driven architecture design.

• Extensive experience in enterprise architecture frameworks (e.g., TOGAF, Zachman).

• Expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) and understanding of deep learning and natural language processing (NLP).

• Strong understanding of big data platforms such as Hadoop, Apache Spark, and data lakes.

• Hands-on experience with cloud platforms (Azure, AWS, or Google Cloud) for building scalable data solutions.

• Proficiency in data modeling, data warehousing, and ETL processes.

• Familiarity with AI ethics and the implications of machine learning and data use in organizational contexts.

• Strong understanding of how AI and data solutions align with and drive business goals and objectives.

• Ability to address complex architectural and business challenges by designing innovative AI-driven solutions.

• Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

Desirable Skills:

• Experience with DevOps practices in AI and data architecture, including continuous integration and deployment (CI/CD) for AI models.

• Familiarity with data privacy regulations (e.g., GDPR, CCPA) and their impact on AI and data architecture.

• Experience with data visualization tools like Power BI, Tableau, or Looker.

• Certifications: Relevant certifications in enterprise architecture, AI, or data science (e.g., TOGAF, Certified Data Management Professional (CDMP), Microsoft Certified: Azure AI Engineer Associate).

Original Posting:
April 16, 2025
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:
Pay Range $126,100.00 - $227,950.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.