Title: AI Adoption Specialist Clearance: Active TS Required, Q Clearance preferred Education: Bachelor’s degree preferred Years Experience: all levels available Location: fully remote Compensation: DOE ($65-$145K/yr.) Overview: An AI adoption specialist is responsible for guiding organizations through the process of integrating AI technologies into their operations. Responsibilities: Assessing readiness: Evaluating the organization's data infrastructure, technology environment, and strategic goals to determine the feasibility of AI adoption. Developing strategies: Creating tailored AI implementation strategies that align with business objectives and technology initiatives. Launching pilots: Conducting small-scale pilots to validate AI concepts, manage risks, and build internal confidence. Full deployment: Overseeing the full deployment of AI solutions, ensuring they are integrated with operations and infrastructure. Training and resources: Providing comprehensive training and resources to equip internal teams with the knowledge and skills to work effectively alongside AI systems. Monitoring and optimization: Tracking system performance, gathering feedback, and refining AI models and processes to maximize performance over time. AI adoption specialists play a crucial role in ensuring that AI technologies are effectively integrated into business operations, leading to improved efficiency, decision-making, and innovation. Note This job description in no way states or implies that these are the only duties to be performed by the employee(s) of this position. Employees will be required to follow any other job-related instructions and to perform any other job-related duties requested by any person authorized to give instructions or assignments. All duties and responsibilities are essential functions and requirements and are subject to possible modification to reasonably accommodate individuals with disabilities. Who we are...  August Schell offers 30 years of experience in providing our customers innovative solutions and engineering services to their most challenging needs. We thrive on navigating complex IT difficulties and are driven to find the best solutions for our customers. Our team delivers expert knowledge in cyber security, big data, endpoint security and cloud solutions. Our ability to serve our customers with continued excellence is a direct reflection of our dedicated and exceptional people. At August Schell, we are not only a team, we are a family. Equal Employment Opportunity August Schell Enterprises Inc. is an Equal Employment Opportunity Employer. We are committed to providing a workplace that is free from discrimination and harassment based on race, color, religion, sex (including pregnancy, sexual orientation or gender identity), national origin, age, marital status, disability, protected veteran status or any other characteristic protected by applicable law. Accommodations If you are an individual with a disability and would like to request a reasonable accommodation during the application or hiring process, please contact Human Resources at hr@augustschell.com

• Define and execute a multi-year AI roadmap aligned to product evolution and regulatory constraints (CMS, Medicaid, HIPAA, NCQA). • Architect scalable ML and generative AI systems embedded in software products. • Evaluate and select appropriate model strategies (predictive models, RAG pipelines, fine-tuned LLMs, reasoning models, agentic workflows). • Recruit and develop a small (4 to start), experienced team of ML engineers and applied AI specialists. • Establish experimentation standards, prompt engineering practices, evaluation benchmarks, and model lifecycle processes. • Create a culture of pragmatic, production-focused AI engineering. • Deliver end-to-end ML/AI solutions, including: risk prediction models, intelligent document summarization, RAG-based or knowledge-graph-based systems, fine-tuned LLM applications, agentic automation workflows. • Ensure solutions are measurable, reliable, and embedded into production workflows. • Establish robust MLOps pipelines (training, validation, deployment, monitoring). • Implement model observability, drift detection, and performance monitoring. • Partner with Security and Compliance to define responsible AI standards (fairness, bias evaluation, explainability, audit trails). • Design PHI-safe AI environments and secure data pipelines. • Partner with Product and Clinical leaders to identify high-value AI use cases. • Translate complex AI concepts into executive-ready business cases. • Guide organizational AI literacy and responsible adoption.

• Design, test, and refine multi-step AI workflows and prompt chains that produce structured, high-quality educational content at scale. • Translate pedagogical and editorial requirements into automated, metadata-driven processes that ensure consistent quality and alignment. • Develop reusable prompt templates, modular frameworks and content generation systems for lessons, quizzes, slides and other formats. • Debug, optimise and document AI workflows, prompts and datasets to improve reliability, speed, traceability and long-term reuse. • Experiment with and evaluate new AI models, tools and APIs and integrate suitable technologies into content production and validation pipelines to improve quality, speed and cost efficiency. • Collaborate with engineers, designers, curriculum specialists and product teams to prototype new AI-driven approaches and internal tooling that enhance both content creation and user-facing features.

• Pioneer the development of Latent Space Models (LSMs) • Build next-generation neural audio codecs • Pioneer steerable generative models • Develop embedding systems for codec latent space • Leverage latent recombination for synthetic audio data • Train multimodal speech-to-speech systems • Design model architectures, training schemes, and inference algorithms