Engineering AI Enablement Consulant

York Solutions; United States; Remoto; USD 145.000 - 161.000; Tempo pieno

Vantaggi offerti da questo lavoro

Flexible remote work

Healthcare options

Career growth

Descrizione del lavoro

York Solutions is seeking a Principal AI and Platform Architect for a remote, seven-week engagement. You will lead technical assessment, reference architecture, and tool evaluations across Google Cloud, AWS, and Azure, delivering an Agentic Engineering Reference Architecture and governance framework.

This hands-on role requires expertise in enterprise architecture, AI-enabled software engineering, and cloud platforms.

Competenze

• Experience leading architecture for enterprise applications and cloud platforms, including identity, APIs, data integration, deployment pipelines, and operational support.
• Practical experience designing or implementing AI applications, agent workflows, or AI-enabled software engineering practices.
• Strong Google Cloud experience, including Vertex AI and Gemini, with working knowledge of AWS and Azure sufficient to assess platform fit and responsibility boundaries.
• Software engineering experience with Java and Spring Boot, Python, and REST APIs. Ability to review code, configuration, scripts, and test results with delivery engineers.
• Experience with source control, automated testing, code review, CI/CD, and engineering quality gates.
• Understanding of AI tool permissions, context and data access, human oversight, evaluation, observability, and failure recovery.
• Ability to lead technical discovery, evaluate unfamiliar tools, and make recommendations within a defined scope and schedule.
• Clear writing and facilitation skills, including the ability to explain architecture choices to business and technology leaders.

Mansioni

• Assess the current environment. Review AI capabilities, application architecture, cloud platforms, data dependencies, engineering workflows, and existing development tools.
• Evaluate platform roles. Assess how Google Cloud, AWS, and Azure should support engineering AI capabilities, considering existing investments, security requirements, integration needs, operating responsibilities, and cost drivers.
• Develop the Agentic Engineering Reference Architecture. Define how models, agents, tools, application data, identity, human approvals, repositories, CI/CD, evaluation, and telemetry fit together. Identify ownership boundaries and follow-on implementation needs.
• Lead development harness and tool evaluation. Create an eligibility checklist and weighted decision framework covering context access, permissions, agent capabilities, testing, observability, developer experience, support, and cost. Distinguish documented capabilities from results observed during validation.
• Define agent autonomy and governance. Establish recommendations for task risk, permitted actions, human approval points, least-privilege access, escalation, stop conditions, recovery, and evidence retention. Align recommendations with existing client governance.
• Guide workflow design and validation. Partner with the AI Tech Lead to shape three executable AI development lifecycle workflow patterns. Review configurations, technical decisions, and evidence from one agreed end-to-end exercise.
• Contribute to the maturity assessment. Evaluate platform, architecture, tooling, and governance capabilities using available evidence. Document gaps, dependencies, confidence levels, and target-state recommendations.
• Translate findings into an actionable roadmap. Connect technical recommendations to the Agentic Engineering Playbook, measurement approach, 90-day activation backlog, and 2027 scale roadmap.
• Communicate decisions clearly. Explain tradeoffs to engineering teams and executives, document recommendations and limitations, and identify decisions that require client ownership.

Conoscenze

Enterprise architecture

Cloud platforms

Java

Python

REST APIs

CI/CD

Security & governance

Strumenti

Google Cloud

Vertex AI

Gemini

AWS

Azure

GitHub Enterprise

GKE

BigQuery

Cloud SQL/PostgreSQL

GitHub Copilot

Microsoft Copilot

Descrizione del lavoro

Description Logistics:
Type: Contract - 7 Weeks - Target Dates 11/16/2026 - 1/4/2027
Location: Remote
Pay Rate: Target $105/hr W2 - $117/hr C2C

Principal AI and Platform Architect

Engineering AI Enablement

York Solutions is seeking a Principal AI and Platform Architect to lead the technical assessment and architecture work for an engineering AI enablement engagement. The initial assignment spans seven weeks and combines capability assessment, platform and tool recommendations, reference architecture, and limited workflow activation.

This is a hands-on architecture role. The architect will work directly with engineering teams to evaluate technical choices, validate assumptions, and produce recommendations supported by implementation evidence.

Responsibilities

• Assess the current environment. Review AI capabilities, application architecture, cloud platforms, data dependencies, engineering workflows, and existing development tools.
• Evaluate platform roles. Assess how Google Cloud, AWS, and Azure should support engineering AI capabilities, considering existing investments, security requirements, integration needs, operating responsibilities, and cost drivers.
• Develop the Agentic Engineering Reference Architecture. Define how models, agents, tools, application data, identity, human approvals, repositories, CI/CD, evaluation, and telemetry fit together. Identify ownership boundaries and follow-on implementation needs.
• Lead development harness and tool evaluation. Create an eligibility checklist and weighted decision framework covering context access, permissions, agent capabilities, testing, observability, developer experience, support, and cost. Distinguish documented capabilities from results observed during validation.
• Define agent autonomy and governance. Establish recommendations for task risk, permitted actions, human approval points, least-privilege access, escalation, stop conditions, recovery, and evidence retention. Align recommendations with existing client governance.
• Guide workflow design and validation. Partner with the AI Tech Lead to shape three executable AI development lifecycle workflow patterns. Review configurations, technical decisions, and evidence from one agreed end-to-end exercise.
• Contribute to the maturity assessment. Evaluate platform, architecture, tooling, and governance capabilities using available evidence. Document gaps, dependencies, confidence levels, and target-state recommendations.
• Translate findings into an actionable roadmap. Connect technical recommendations to the Agentic Engineering Playbook, measurement approach, 90-day activation backlog, and 2027 scale roadmap.
• Communicate decisions clearly. Explain tradeoffs to engineering teams and executives, document recommendations and limitations, and identify decisions that require client ownership.

Required Experience

• Experience leading architecture for enterprise applications and cloud platforms, including identity, APIs, data integration, deployment pipelines, and operational support.
• Practical experience designing or implementing AI applications, agent workflows, or AI-enabled software engineering practices.
• Strong Google Cloud experience, including Vertex AI and Gemini, with working knowledge of AWS and Azure sufficient to assess platform fit and responsibility boundaries.
• Software engineering experience with Java and Spring Boot, Python, and REST APIs. Ability to review code, configuration, scripts, and test results with delivery engineers.
• Experience with source control, automated testing, code review, CI/CD, and engineering quality gates.
• Understanding of AI tool permissions, context and data access, human oversight, evaluation, observability, and failure recovery.
• Ability to lead technical discovery, evaluate unfamiliar tools, and make recommendations within a defined scope and schedule.
• Clear writing and facilitation skills, including the ability to explain architecture choices to business and technology leaders.

Preferred Experience

• Google ADK, Vertex AI agent tooling, agent SDKs, or agent management frameworks.
• GitHub Enterprise, GitHub Copilot, Microsoft Copilot, GKE, Cloud SQL/PostgreSQL, and BigQuery.
• Architecture and delivery work in healthcare or other regulated enterprise environments.
• Engineering maturity assessments, technology decision frameworks, and adoption roadmaps.
• Evaluating AI engineering outcomes through delivery, quality, intervention, adoption, and cost measures.

Working Relationships

The architect will report to the Engagement Lead and own the technical architecture and platform recommendations. The AI Tech Lead will lead workflow configuration and execution, with the architect providing design guidance, technical validation, and review. The architect will also work with the Business Analyst and Delivery Coordinator on maturity findings, measurement, adoption implications, and roadmap documentation. Specialist advisors will provide targeted security, identity, data, and cloud expertise, with executive advisors participating in key reviews.

Expected Results During the Initial Engagement

• An Agentic Engineering Reference Architecture with clear dependencies, controls, and ownership boundaries.
• A documented development harness and tool decision framework with recommendations and supporting rationale.
• An agent autonomy and governance model connected to the agreed workflow patterns.
• Technical guidance and review supporting three runnable patterns and one documented end-to-end exercise.
• Evidence-based maturity findings and prioritized technical actions for the 90-day backlog and 2027 roadmap

York Solutions Offers a generous benefits package for eligible full-time employees:

• BCBS Medical with 3 Plans to choose from (PPO and High deductible PPO plans with Health Savings Program)
• Delta Dental plan with 2 free cleanings and insurance discounts
• Eye Med Vision with annual check-ups and discounts on lens
• Life and Accidental Death Insurance paid by company
• John Hancock 401(k) Retirement Plan with discretionary company match
• Voluntary Insurance programs such as: Hospital Indemnity, Identity Protection, Legal Insurance, Long Term Care, and Pet Insurance.
• Flexible work environment with some remote working opportunities
• Strong fun and teamwork environment
• Learning, development, and career growth