- Technology Selection Process (TSP) Analysis: Analyze the current TSP workflows and identify areas for enhancement to meet AI remediation requirements for acquiring new products and services with models.
- AI Controls Development: Develop and maintain AI controls within the architecture profile and/or template. Ensure that the overall design solution undergoes P/EARB review whenever there are one or more models involved. Validate the AI/ML requirements during the architecture review board (ARB).
- AI Controls Update: Regularly update and refine the AI controls, incorporating them into the architecture profile and template to ensure compliance and alignment with evolving needs and requirements.
- Data Synchronization: Collaborate with stakeholders to analyze the current and target states of the Approved Software List (ASL), Business Assets Registry (BAR), and Model Usage Inventory Management (MUSE) data synchronization. Identify areas for enhancement to meet AI remediation requirements and ensure effective synchronization between the ASL, BAR, and MUSE systems.
- Enterprise Architecture Policies: Work with the enterprise architecture policies and standards team to incorporate AI-related requirements specified and needed during the technology selection and architecture review processes.
- Documentation and Training: Document and socialize the revised TSP workflows, incorporating models, and provide training to stakeholders on their utilization.
- Collaboration with ADAG Stakeholders: Collaborate with adaptive digital architecture governance (ADAG) stakeholders, including the process, architecture, and model review teams, to gather input and feedback on the controls required to revise the architecture profile and/or template during the architecture review process.
- Data Synchronization Implementation: Collaborate with stakeholders to define and implement the necessary data synchronization requirements to synchronize data between the ASL, BAR, and MUSE systems.
- Workstream Maintenance: Participate in developing and maintaining the plan for the listed workstreams.
- SDLC: Strong understanding of the Software Development Life Cycle.
- Requirement Gathering: Proficiency in requirement gathering and documentation.
- Enterprise Architecture: Expertise in enterprise architecture, with a focus on AI models and controls.
- Model-Based Development: Knowledge and experience in model-based development and reviewing, specifically related to AI models.
- Gen AI Models: Knowledge of various Gen AI Models (LLAMA / OpenAI / Google Bard / Mistral etc).
- Data Synchronization: Understanding of data synchronization principles and techniques.
- Adaptability: Adaptability and flexibility to evolving needs and requirements.
- Collaboration: Strong collaboration and communication skills.
- ServiceNow: Optional: Familiarity with ServiceNow.
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Sr. Artificial Intelligence Machine Learning Architect - Herndon, United States - Navitas
Description
Sr. AI/ML Analyst/Architect
Location: Remote
Navitas Business Consulting is seeking a Sr. Artificial Intelligence Machine Learning Architect. This role would cover several areas and focus on A.I/Machine Learning platforms.
Job Responsibilities:
Skills:
This role requires a candidate who is not only technically proficient but also has a strategic mindset to analyze and enhance workflows, develop and maintain AI controls, and collaborate effectively with various stakeholders. The candidate should be adaptable and flexible to evolving needs and requirements, and have strong collaboration and communication skills. Familiarity with ServiceNow is a plus. This is a challenging role that offers the opportunity to work at the forefront of AI/ML technology and its application within an enterprise architecture context.
