HonorVet Technologies is a Service Disable Veteran-Owned IT staffing firm, ISO 9001 and ISO 27001 certified, working with federal agencies, state governments, and Fortune 500 enterprise clients across the US. What makes us different isn't a tagline; it's the way we work. We don't forward resumes and hope for the best. We take the time to understand where a professional like you is headed and only reach out when we genuinely believe there's a fit worth exploring. I came across your profile, and something stood out.
Title/Level: Artificial Intelligence/Machine Learning Engineer Specialist
Location: North Austin, Texas 78758Duration: 03 months to start with the possibility of extensionNote: Open To Austin, Texas Residents Only - Hybrid, In-Office - 3 Days Per Week
Description:The Forward Deployed Engineer (FDE) works directly with client and partner agencies to rapidly design, build, deploy, and iterate modern digital solutions—often working onsite or embedded with mission teams. The FDE combines hands-on engineering, problem solving, rapid prototyping, and production deployment to accelerate modernization and reduce dependency on legacy integrator models.
The FDE bridges gaps between product teams, security, business units, and cloud engineering— The FDE should apply platform-agnostic engineering practices and evaluate AI/LLM capabilities based on business need, security requirements, data classification, interoperability, sustainability, and total cost of ownership rather than defaulting to a single cloud, model, or vendor ecosystem. Provides FDE methodology and best practices to client staff for knowledge transfer sessions and skill growth. Supports IT and other AI initiative at client.
This role is intended to bring advanced, forward-looking technical capability to client and partner agencies while remaining flexible, platform-agnostic, and outcomes-focused. The consultant should be able to work at the intersection of modern software engineering, cloud-native architecture, AI-enabled development, automation, security, and agency mission delivery. Rather than prescribing a specific cloud platform, LLM provider, or toolchain, the role should emphasize the ability to evaluate technologies based on business need, security posture, data sensitivity, interoperability, cost, operational maturity, and long-term sustainability.
The ideal candidate should help client and agencies understand what is possible with modern technology, translate emerging capabilities into practical delivery patterns, and coach internal teams on how to adopt those capabilities responsibly. This includes helping teams turn ambiguous problems into practical, AI-enabled workflows, while exploring AI, automation, APIs, integration patterns, DevSecOps, and reusable components. Focus on rapid prototyping and delivering value without assuming any single vendor or solution is always the right fit. The goal is to raise technical fluency, accelerate modernization, and build internal capability while preserving architectural flexibility. The role should be aspirational in terms of skill level and innovation, but not overly prescriptive in terms of specific products, platforms, or implementation methods.
Deliverables- Production-ready code, pipelines, infrastructure templates, and documentation.
- Architecture diagrams, operational runbooks, and security compliance mappings.
- AI-assisted development workflows and accelerators.
- Knowledge transfer sessions and training for agency development staff.
Key Responsibilities- Deliver high-quality application, Application Programming Interface (API), Model Context Protocol (MCP), and automation components using cloud-native architectures.
- Develop rapid prototypes, pilots, and production systems using modern engineering patterns.
- Integrate systems across agencies using secure, scalable, human-in-the-loop workflows.
- Implement DevSecOps automation (CI/CD, IaC, container orchestration, cloud pipelines).
- Collaborate directly with agency stakeholders to gather requirements and convert them into working software.
- Deploy AI-enabled development workflows and LLM-assisted capabilities.
- Troubleshoot complex production issues and lead root-cause analysis.
- Mentor agency developers, maturing internal capability and reducing vendor reliance.
- Provide documentation, architectural guidance, and knowledge transfer.
- Rapidly build AI-powered tools using existing systems, and create new applications where needed, to move from experimentation to real impact.
- Comfort working across cloud environments and internal enterprise systems.
II. CANDIDATE SKILLS AND QUALIFICATIONSMinimum Requirements:
Candidates that do not meet or exceed the minimum stated requirements (skills/experience) will be displayed to customers but may not be chosen for this opportunity. |
| Years | Required/Preferred | Experience |
| 8 | Required | Hands-on software engineering experience. |
| 8 | Required | Expertise in modern cloud platforms. |
| 8 | Required | Strong proficiency in: TypeScript/JavaScript, Python, or C#; Modern UI frameworks (React, Angular, Web Components). |
| 8 | Required | Experience with integrating APIs (LLMs, internal services, data platforms). |
| 8 | Required | Experience with CI/CD platforms using GitHub Actions, Azure DevOps, or equivalent including building and deploying applications. |
| 8 | Required | Experience with infrastructure as code and automating environments (e.g., Terraform, ARM/Bicep, or similar tools. Experience working directly with customers or frontline operational teams to build and improve solutions. |
| 8 | Required | Extend tools like Salesforce, Appian, ServiceNow, etc. Demonstrated success delivering systems end-to-end from design → deploy. |
| 8 | Required | Understanding of security frameworks (NIST, Zero Trust, TX-RAMP expectations). |
| 8 | Required | Excellent communication and cross-functional collaboration skills. |
| 8 | Required | Ability to decide when NOT to use low-code. |
| 8 | Required | Ability to identify high-value use cases and ability to observe workflows. |
| 8 | Required | Bachelor's degree in Computer Science, Engineering, or related field OR • Equivalent experience (10+ years) in hands-on modern engineering roles. |
| 8 | Preferred | Experience in state government, regulated environments, or multi-agency integration projects. |
| 8 | Preferred | Prior FDE or technical field engineering experience at a software platform company. |
| 8 | Preferred | Experience designing, evaluating, or implementing AI-enabled workflows using commercial, open-source, or government-approved LLM platforms, including patterns such as retrieval-augmented generation, agentic workflows, model evaluation...cont. next line... |
| 8 | Preferred | prompt management, human-in-the-loop review, and responsible AI controls. Experience with shared technical services or modernization programs (e.g., TSS/MSI) . |
| 8 | Preferred | Experience producing reusable components, design systems, developer tooling. |
| 8 | Preferred | Ability to compare AI/LLM options using objective criteria such as data sensitivity, hosting model, latency, cost, accuracy, explainability, auditability, security controls, integration complexity, and operational sustainability. |
| 8 | Preferred | CISSP, CCSP, or CISM |
| 8 | Preferred | Kubernetes certifications (CKA/CKAD) |
| 8 | Preferred | TOGAF or architecture certifications |
| 8 | Preferred | Scrum Master or SAFe Agile certs |
| 6 | Preferred | TX-RAMP knowledge or auditor training |
| 1 | Preferred | Cloud architecture, DevOps, AI, security, or Kubernetes certifications from one or more major providers, such as Azure, AWS, Google Cloud, Kubernetes, HashiCorp, ISC2, ISACA, or equivalent. |