AI Specialist – Cloud & AI Solutions

Overview

SLAC Job Postings
Position Overview Join the dynamic IT team at SLAC National Accelerator Laboratory and take your career to the next level as our AI Specialist! We are seeking a highly skilled and motivated AI Specialist with experience on AWS to join our Cloud Services team. In this role, you will design, build, deploy, and operationalize AI/ML solutions that drive business value, working closely with cross-functional stakeholders, data engineers, Dev Ops, and our platform teams (on-prem and cloud).

As an AI Specialist, you will drive innovation, apply best practices in machine learning (ML) and AI, and ensure the successful integration of AI into our cloud environment.

You’ll have the chance to collaborate with a talented team of professionals from various disciplines, exchanging ideas and driving initiatives that enable our organization’s goals. Your expertise and problem-solving skills will be essential as we optimize performance, scalability, and efficiency.

• Lead the end-to-end development of AI/ML solutions on AWS: from problem definition, data ingestion, feature engineering, model development/training, to deployment and monitoring in production.
• Collaborate with business stakeholders, data scientists, software engineers, and platform teams (on-premises + cloud) to translate business requirements into scalable, performant AI solutions.
• Design and implement data pipelines, orchestration, model training, evaluation, and deployment leveraging AWS native services (e.g., Sage Maker, EC2, S3, Glue, Lambda, Athena, Redshift) and integrate with on-premises or hybrid infrastructure.
• Optimize and fine-tune machine learning models (supervised, unsupervised, deep learning, NLP, computer vision) to maximize accuracy, efficiency, and cost-effectiveness.
• Deploy AI/ML models into production environments: set up CI/CD, MLOps practices, automated monitoring, model-drift detection, logging, alerting and lifecycle management.
• Ensure robust, secure, scalable, compliant architecture of AI systems in a cloud context (AWS best practices for security, cost optimization, IAM, networking, governance).
• Work with Dev Ops/platform teams to ensure smooth integration of AI workloads into the overall cloud platform, including hybrid on-premises components (given your dual-environment experience).
• Provide guidance, mentoring, and best-practice evangelism in AI/ML, cloud architecture, MLOps, and AWS services to other team members and stakeholders.
• Stay up to date on new and emerging AI/ML and AWS technologies, evaluate their applicability, and make recommendations for adoption or a proof of concept.
• Prepare and deliver technical presentations, documentation, model-explainability reports, and training workshops for diverse audiences (technical and non-technical).
• Maintain and uphold ethical AI practices, ensuring model transparency, fairness, accountability, interpretability, and compliance with institutional policies.

• Bachelor’s degree in Information Technology, or a related field, and ten years of increasingly technical work experience, or a combination of education and relevant experience.
• Proven experience with AWS services for ML/AI (for example: Sage Maker, EC2, S3, Glue, Athena, Lambda, Redshift, etc).
• Strong programming skills in Python (and/or R/Java/Scala) and experience with ML/AI frameworks.
• Experience with data engineering: data ingestion, cleaning, transformation, feature engineering, large-scale datasets, data warehousing/Hadoop/Spark as applicable.
• Experience deploying models into production, establishing ML operational pipelines (CI/CD, monitoring, drift detection, logging), and maintaining them.
• Understanding of cloud architecture best practices (AWS), including networking, security (IAM, encryption, VPC, roles), cost optimization, and performance tuning.
• Strong analytical and problem-solving skills, with the ability to work in cross-functional teams and interact with stakeholders at various levels.
• Excellent communication and documentation skills — ability to explain complex technical concepts to business and technical audiences.
• Demonstrated self-learning and adaptability in…