Description
Leidos is looking for an Enterprise Architect to support a large U.S. Department of Justice (DOJ) program. The Antitrust Division of the U.S. Department of Justice (DOJ) is responsible for enforcing federal antitrust laws and promoting fair competition in the marketplace. The Enterprise Architect will be responsible for shaping and leading the architectural vision and strategy around AI and data solutions across the organization. This position will involve working closely with business stakeholders, data scientists, and IT teams to design and implement scalable, secure, and efficient architectures that leverage AI technologies and data-driven insights. You will use your deep knowledge of enterprise architecture principles, AI technologies, and data management practices to guide decision-making and ensure that the architecture supports business objectives and enhances operational capabilities. This work is located onsite in the DC area.

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).

• 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.