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Enterprise Architect
| Company | Leidos |
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| Location | Washington, DC, USA |
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| Salary | $126100 – $227950 |
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| Type | Full-Time |
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| Degrees | Bachelor’s, Master’s |
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| Experience Level | Senior, Expert or higher |
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Requirements
- 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.
Responsibilities
- Design and develop end-to-end AI and data architectures 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.
Preferred Qualifications
- 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).
