Innovation Engineer- Remote (Anywhere in the U.S.)

General Description

GuidePoint Security is seeking a skilled and security-conscious Innovation Engineer to join our technology team. This role is pivotal in building and scaling our generative AI capabilities. You will be responsible for the design, implementation, security, and operational management of generative AI solutions that will be leveraged internally. You will work closely with IT infrastructure and information security teams to ensure all implementations adhere to enterprise security standards, company policies, and industry best practices. This role offers the exciting opportunity to contribute to our AI strategy and potentially expand into more advanced ML implementations as the program and capabilities mature.

The Innovation Engineer will be required to demonstrate patience, self-motivation, and strong business/ financial acumen.

Roles and Responsibilities:

Design & Implement GenAI Solutions: Help design, build, deploy, and manage secure and scalable generative AI solutions using both SaaS and local resources.

Enable Technical Users: Provide guidance, best practices, and support to internal teams utilizing SaaS AI services to build custom applications.

Data Integration: Design and assist with implementing secure data connectors and ingestion pipelines to allow enterprise AI services to query internal organizational data sources (e.g., knowledge bases, document repositories).

Security & Compliance: Collaborate closely with Information Security and IT teams to define security requirements, implement robust security controls (IAM policies, network configurations, data encryption, logging, monitoring), conduct security reviews, and ensure compliance with internal policies and relevant regulations for all AI deployments.

Operational Excellence: Assist as needed in establishing monitoring and alerting for applicable AI solutions and help develop operational procedures for deployed AI services. Optimize for performance, scalability, and cost-effectiveness.

Collaboration: Act as a liaison between business stakeholders, technical teams, IT operations, and information security regarding generative AI initiatives.

Stay Current: Keep abreast of the latest developments in SaaS AI/ML services, generative AI trends, and cloud security best practices.

Documentation: Create and maintain clear technical documentation, architecture diagrams, and security guidelines.

Future Planning: Contribute to the strategic roadmap for AI/ML within the organization.

Facilitate Education: Assist with maintaining, developing, and presenting educational material to internal users about effective and safe usage of AI.

Required Experience and Education:

5+ years of experience in cloud engineering and/or solutions architecture with a significant focus on AWS

Deep hands-on experience specifically implementing, managing, and supporting AI solutions using AWS services within an enterprise context

Strong understanding and practical experience with core AWS services (e.g., IAM, DynamoDB, S3, Lambda, CloudWatch, CloudTrail)

Proven experience designing and implementing secure, cloud-based AI solutions on AWS, including familiarity with AWS security services (e.g., Guardrails, KMS, Secrets Manager)

Enterprise-level experience with AI-focused AWS services such as Bedrock, SageMaker, Transcribe, Rekognition, and Q Business

Demonstrated experience working collaboratively with IT Operations and Information Security teams on cloud deployments and security reviews

Proficiency in at least one relevant programming language, preferably Python

Solid understanding of generative AI concepts, Large Language Models (LLMs), prompt engineering, and foundational AI/ML principles

Excellent problem-solving skills, the ability to troubleshoot complex technical issues, and the patience to come up with creative solutions that work for all stakeholders within policy boundaries

Strong written and oral communication and interpersonal skills, with the ability to explain complex technical concepts to both technical and non-technical audiences

Demonstrated experience applying security principles to AI implementations, including data protection, access controls, and threat modeling for AI systems

Understanding of AI-specific security challenges including prompt injection, data poisoning, and model extraction attacks

Preferred Experience and Education

AWS Certified Cloud Practitioner

AWS Certified AI Practitioner

AWS Certified Solutions Architect

AWS Certified Machine Learning Engineer

Understanding or experience with model fine-tuning techniques

Experience with Infrastructure as Code (IaC) tools like AWS CloudFormation, Terraform, OpenTofu, or equivalent technologies

Familiarity with MLOps principles and practices

Experience integrating AI services with enterprise applications and data warehouses

Experience designing and implementing agentic AI architectures that can autonomously perform complex workflows while maintaining appropriate security boundaries and human oversight

Familiarity with MCP client/server architecture and the associated security risks

Travel Requirements:

Up to 10% travel

Physical Requirements:

Sedentary work

Substantial movement of the wrists, hands, and/or fingers for a minimum of 8 hours a day

Required to have close visual acuity to view computer terminal and/or extensive reading for a minimum of 8 hours a day