About the job
Designs and implements effective AI solution architecture or strategy utilizing approaches of various AI technologies and methods to address clients' business problems and needs, while complying with the organization’s strategies and business goals, and key ethical considerations. Applies leading-edge principles, theories, and concepts, and contributes to the development of new principles and concepts. Works on unusually complex problems and provides highly innovative solutions. Operates with substantial latitude for unreviewed action or decision, and mentors or supervises employees in both company and technical competencies.
Responsibilities
Designing, developing, deploying, and maintaining Generative AI and AI Agents employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), Custom ML modeling, and training
Building and scaling AI-ready CI/CD pipelines leveraging IaaS for MLOps and AIOps, and leveraging DevSecOps tools to manage the end-to-end deployment of AI prototypes into highly regulated production environments while maintaining high availability and security compliance
Developing solutions using technologies or tools, including Kubernetes, Docker, Terraform, Helm, Linux, AWS Cloud Services such as Lambda, DynamoDB, S3, SageMaker, CloudWatch, and Bedrock
Translating complex AI concepts and the AI/ML development lifecycle into actionable business insights for senior-level leadership and operational end-users, facilitating informed decision-making and project buy-in
Qualifications
Minimum
8+ years of experience with data science, machine learning (ML), or deep learning
3+ years of experience with AI on AWS services and platforms
Experience designing, developing, deploying, and maintaining Generative AI and AI Agents employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), Custom ML modeling, and training
Experience building and scaling AI-ready CI/CD pipelines leveraging IaaS for MLOps and AIOps, and leveraging DevSecOps tools to manage the end-to-end deployment of AI prototypes into highly regulated production environments while maintaining high availability and security compliance
Ability to develop solutions using technologies or tools, including Kubernetes, Docker, Terraform, Helm, Linux, AWS Cloud Services such as Lambda, DynamoDB, S3, SageMaker, CloudWatch, and Bedrock
Ability to translate complex AI concepts and the AI/ML development lifecycle into actionable business insights for senior-level leadership and operational end-users, facilitating informed decision-making and project buy-in
Top Secret clearance
Bachelor’s degree
AWS Certified Solutions Architect Certification
Preferred
Experience with MLOps for production and ML workloads
Experience working with cybersecurity teams to develop and review technical documentation and security control implementation required to achieve and maintain ATO
Experience packaging and framing technical R&D outputs into enterprise-grade offerings, ensuring AI solutions are structured for governance, security compliance, and seamless alignment with existing business workflows