Job Title: Solution Architect – AI
Location: Remote / Eastern or Central Time Zone preferred (Periodic travel to Buffalo, NY every 4–6 weeks required)
Role Type: Contract to hire
About the Role:
We are seeking an experienced Solution Architect for AI to serve as the critical technical bridge between our enterprise AI strategy and engineering execution. Acting as the solution architecture extension of the AI Enterprise Architect, you will translate conceptual, logical, and high-level physical designs into detailed, production-ready physical architectures for the Mythos AI platform.
In this role, you will provide hands-on architecture guidance, guide engineering teams through the Software Development Life Cycle (SDLC), and ensure our AI solutions are scalable, secure, cost-effective, and fully aligned with enterprise governance frameworks.
Key Responsibilities:
- Detailed Technical Design: Transform high-level enterprise designs into comprehensive physical architectures for the Mythos AI platform, covering core components such as model integration, secure access controls, APIs, and embedded AI capabilities within business applications.
- Hands-On Architecture Guidance: Drive the implementation of core AI platform capabilities, including:
- Model Access & Lifecycle Integration: Designing execution patterns, including Small Language Model (SLM) deployments.
- Telemetry & Observability: Instrumenting tracking for model usage, performance metrics, and risk signals.
- Guardrail Enforcement: Establishing robust policy controls, data protection mechanisms, and safe usage constraints.
- Application Integration: Defining integration patterns to seamlessly embed AI into broader enterprise software.
- Decision Framework Translation: Operationalize model selection frameworks and decision trees into actionable design blueprints, balancing cost, performance, and risk objectives.
- SDLC Partnership & Oversight: Collaborate with engineering teams from project initiation to production, offering design oversight, resolving implementation challenges, and validating operational readiness.
- Security & Reliability Advocacy: Embed secure engineering and site reliability engineering (SRE) best practices—such as threat modeling, vulnerability mitigation, and proactive monitoring—into every layer of the architecture.
- Feedback & Evolution: Partner with Enterprise Architecture (EA), platform engineering, and SRE teams to drive standard adoption while feeding practical implementation insights back into future architecture patterns.
Required Qualifications & Skills:
Experience: Proven track record in designing and building scalable enterprise AI solutions, platforms, or integrated machine learning systems.
Technical Expertise: Deep knowledge of AI/ML model deployment patterns, LLM/SLM execution, AI model lifecycle management, and API design.
Security & Governance: Strong understanding of AI guardrails, policy enforcement, data privacy, and secure engineering practices (threat modeling, vulnerability management).
Observability & Operations: Familiarity with telemetry tools and SRE principles tailored to AI workloads (monitoring drift, latency, usage, and risk signals).
Communication & Collaboration: Exceptional interpersonal and communication skills, with the ability to bridge the gap between high-level enterprise strategy and hands-er engineering execution.
EEO: “Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”
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