Innovation Engineer

Responsibilities

• Design, build, deploy, manage, and scale secure generative AI solutions using AWS, SaaS, and local resources.
• Guide internal technical users on best practices and support for SaaS AI services and custom applications.
• Design and implement secure connectors and ingestion pipelines for enterprise data sources such as knowledge bases and document repositories.
• Define and implement AI security controls, including IAM policies, network configurations, encryption, logging, monitoring, security reviews, and compliance measures.
• Establish monitoring, alerting, and operational procedures while optimizing AI services for performance, scalability, and cost.
• Coordinate generative AI initiatives among business stakeholders, technical teams, IT Operations, and Information Security.
• Maintain technical documentation, architecture diagrams, security guidelines, and internal AI education materials.
• Track AI and cloud security developments and contribute to the organization’s AI/ML roadmap.

Requirements

• 5+ years of cloud engineering and/or solutions architecture experience with significant AWS focus.
• Hands-on enterprise experience implementing, managing, and supporting AI solutions using AWS services.
• Strong practical knowledge of AWS IAM, DynamoDB, S3, Lambda, CloudWatch, and CloudTrail.
• Experience designing secure cloud-based AI solutions and using AWS security services such as Guardrails, KMS, and Secrets Manager.
• Enterprise experience with AWS Bedrock, SageMaker, Transcribe, Rekognition, and Q Business.
• Experience collaborating with IT Operations and Information Security teams on cloud deployments and security reviews.
• Proficiency in at least one relevant programming language, preferably Python.
• Understanding of generative AI, large language models, prompt engineering, and foundational AI/ML principles.
• Experience applying security principles to AI systems, including data protection, access controls, threat modeling, prompt injection, data poisoning, and model extraction defenses.
• Strong troubleshooting, problem-solving, written communication, oral communication, and stakeholder collaboration skills.
• Preferred: AWS Certified Cloud Practitioner, AWS Certified AI Practitioner, AWS Certified Solutions Architect, or AWS Certified Machine Learning Engineer.
• Preferred: model fine-tuning, Infrastructure as Code, MLOps, enterprise application and data warehouse integration, agentic AI architecture, or MCP client/server architecture experience.

Benefits

• Primarily remote work within the U.S.; some travel or on-site work may be required for certain positions.
• Up to 10% travel.
• Medical insurance options including a zero-deductible PPO and a high-deductible HSA plan with employer premium contributions.
• Dental insurance with employer-paid employee premiums and partial family-plan contributions.
• 12 corporate holidays and a Flexible Time Off program.
• Mobile phone and home internet allowance.
• Retirement plan eligibility after two months at open enrollment.
• Pet benefit option.