AI Specialist

** Job Description*
* ** Position Overview*
* Join the IT team at SLAC National Accelerator Laboratory as an AI Specialist within our AI/Cloud Services team. We are seeking a highly skilled, collaborative, and motivated professional with experience designing and operationalizing artificial intelligence and machine learning solutions across Amazon Web Services (AWS), Google Cloud Platform (GCP), and hybrid on-premises environments.

In this role, you will help establish and expand SLAC's enterprise AI capabilities in support of scientific research, accelerator operations, and administrative functions. You will work closely with researchers, business stakeholders, data scientists, software engineers, cybersecurity, Dev Ops engineers, and cloud platform teams to translate complex needs into secure, scalable, and sustainable AI solutions.

The successful candidate will combine hands-on technical expertise with strong communication, consulting, and problem-solving skills. You will help teams move from experimentation and proofs of concept to reliable production services, while promoting reusable platforms, responsible AI practices, and appropriate governance. Because SLAC's AI and cloud capabilities continue to evolve, this position requires someone who is comfortable working through ambiguity, evaluating emerging technologies, and helping colleagues develop their skills.

** Your specific responsibilities will include:*
* + Lead the end-to-end development and operationalization of AI and machine learning solutions across AWS, GCP, and hybrid environments, including problem definition, data ingestion, feature engineering, model development, evaluation, deployment, monitoring, and lifecycle management.

+ Partner with researchers, business stakeholders, data scientists, software engineers, cybersecurity, and platform teams to understand requirements and translate them into scalable, secure, cost-effective, and supportable AI solutions.

+ Design and implement data pipelines, orchestration workflows, model-training environments, evaluation processes, and deployment architectures using cloud-native services.

+ Use AWS services such as Amazon Bedrock, Sage Maker, EC2, S3, Glue, Lambda, Athena, Redshift, Step Functions, and related analytics, security, and monitoring services.

+ Use Google Cloud services such as Vertex AI, Gemini, Big Query, Cloud Storage, Dataflow, Dataproc, Cloud Run, Cloud Functions, Pub/Sub, and related analytics, security, and monitoring services.

+ Develop and support generative AI solutions, including retrieval-augmented generation, enterprise search, prompt management, model routing, model evaluation, guardrails, and AI agents and workflows.

+ Evaluate and optimize traditional machine learning and generative AI models for accuracy, reliability, latency, scalability, security, and cost-effectiveness.

+ Establish MLOps and LLMOps capabilities, including source control, infrastructure as code, CI/CD, automated testing, model and prompt versioning, evaluation, observability, drift detection, logging, alerting, and rollback procedures.

+ Design solutions that integrate cloud AI services with SLAC's on-premises infrastructure, enterprise applications, scientific data sources, identity systems, networking, and security services.

+ Apply cloud architecture and security best practices, including identity and access management, least-privilege access, encryption, secrets management, network segmentation, data protection, audit logging, compliance, resilience, and cost governance.

+ Help develop reusable AI platforms, reference architectures, templates, APIs, and shared services that enable SLAC teams to innovate without creating unnecessary duplication or isolated solutions.

+ Work with cybersecurity, privacy, legal, data owners, and governance stakeholders to assess data sensitivity, third-party model usage, information-sharing requirements, intellectual property considerations, and other AI-related risks.

+ Promote responsible AI practices, including transparency, human oversight, explainability, fairness, accountability, privacy, security, and appropriate documentation of model limitations.

+ Conduct technical evaluations and proofs of concept for emerging AI/ML technologies and provide clear recommendations based on business value, scientific value, risk, supportability, interoperability, and total cost of ownership.

+ Troubleshoot complex technical issues spanning AI models, data pipelines, cloud services, APIs, networking, identity, security, and hybrid infrastructure.

+ Provide technical leadership, mentoring, and knowledge sharing to team members who are developing their cloud, data, and AI skills.

+ Create and maintain architecture diagrams, technical standards, operational runbooks, support procedures, model documentation, decision records, and service documentation.

+ Prepare and deliver technical presentations, demonstrations, training workshops, and model-explainability reports for technical and non-technical…