AI Specialist

Abundance Network; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

SLAC National Accelerator Laboratory is seeking an AI Specialist to lead end-to-end AI/ML development across AWS, GCP, and hybrid environments. You will collaborate with researchers, data scientists, software engineers, and IT platform teams to translate complex needs into scalable, secure AI solutions.

The role emphasizes production deployment, responsible AI practices, and the establishment of reusable platforms and governance. Strong communication and mentoring skills are required.

Qualifications

• Experience designing, building, deploying AI/ML in production cloud environments.
• Proven ability to translate complex needs into scalable AI solutions.
• Strong communication and collaboration across diverse teams.
• Familiarity with governance, security, and responsible AI practices.

Responsibilities

• Lead end-to-end development and operationalization of AI/ML across AWS/GCP/hybrid environments.
• Collaborate with researchers, data scientists, and platform teams to define requirements.
• Design data pipelines, training environments, and deployment architectures.
• Establish MLOps/LLMOps capabilities including CI/CD, monitoring, logging.
• Integrate cloud AI services with on-premises infrastructure and security controls.
• Provide technical leadership and mentorship; create runbooks and documentation.

Skills

AI/ML design

Production deployment

Python programming

Cloud platforms (AWS/GCP)

Data pipelines

MLOps/LLMOps

Security/compliance

Technical leadership

Problem solving

Communication skills

Education

Bachelor's degree in IT/CS/Data Science/Engineering

10+ years of relevant experience

Tools

Amazon Bedrock

SageMaker

Vertex AI

BigQuery

Spark

Terraform

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, DevOps 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, SageMaker, EC2, S3, Glue, Lambda, Athena, Redshift, Step Functions, and related analytics, security, and monitoring services.
• Use Google Cloud services such as Vertex AI, Gemini, BigQuery, 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 audiences.
• Collaborate with cloud providers, consultants, vendors, Stanford University partners, and other external organizations while ensuring that SLAC retains the knowledge needed to operate and support its services.
• Stay current with developments across AWS, Google Cloud, open-source AI frameworks, foundation models, AI agents, data platforms, and responsible AI practices.

To be successful in this position, you will bring:

• A bachelor’s degree in information technology, computer science, data science, engineering, or a related field and ten years of increasingly responsible technical experience, or an equivalent combination of education and relevant experience.
• Demonstrated experience designing, building, deploying, and supporting AI/ML solutions in production cloud environments.
• Substantial experience with AWS or GCP AI/ML and data services, along with the ability and willingness to develop proficiency across both platforms.
• Experience with relevant AWS technologies such as Amazon Bedrock, SageMaker, S3, Glue, Lambda, Athena, Redshift, and related services.
• Experience with relevant Google Cloud technologies such as Vertex AI, Gemini, BigQuery, Cloud Storage, Dataflow, Cloud Run, Pub/Sub, and related services.
• Strong programming skills in Python and experience with relevant languages or frameworks such as SQL, Java, R, Scala, PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, or similar technologies.
• Experience developing generative AI applications using foundation models, APIs, embeddings, vector databases, retrieval-augmented generation, prompt engineering, model evaluation, and AI agent or workflow frameworks.
• Experience with data engineering, including data ingestion, cleansing, transformation, metadata, feature engineering, data quality, large-scale datasets, data warehouses, and distributed processing technologies such as Spark.
• Experience deploying and maintaining models in production through MLOps or LLMOps practices, including CI/CD, automated testing, monitoring, evaluation, drift detection, logging, and lifecycle management.
• Experience with infrastructure as code and automation technologies such as Terraform, CloudFormation, AWS CDK, or Google Cloud deployment tooling.
• Understanding of cloud architecture practices across networking, identity and access management, encryption, secrets management, observability, resilience, performance optimization, and cost management.
• Experience integrating cloud services with on-premises systems in a hybrid enterprise environment.
• Knowledge of data governance, privacy, cybersecurity, responsible AI, and risk-management principles applicable to enterprise and research environments.
• Strong analytical and troubleshooting skills, including the ability to diagnose issues that cross application, data, model, cloud-platform, security, and network boundaries.
• Strong written and verbal communication skills, with the ability to explain complex technical concepts, risks, limitations, and tradeoffs to both technical and non-technical audiences.
• Demonstrated ability to document solutions thoroughly and create operationally useful architecture diagrams, standards,