AI Solutions Design Manager - Hybrid

📍 Job Overview

Job Title: AI Solutions Design Manager - Hybrid

Company: Mindex

Location: Rochester, New York, United States

Job Type: Full-Time

Category: AI & Machine Learning / Operations Strategy

Date Posted: August 12, 2026

Experience Level: 7+ Years

Remote Status: Hybrid

🚀 Role Summary

• Lead the strategy, design, and delivery of cutting-edge AI and Generative AI solutions to drive significant business value and enhance operational performance across the organization.

• Architect and implement scalable AI/GenAI systems, including chatbots, copilots, and content-generation workflows, leveraging modern platforms and methodologies.

• Manage a portfolio of AI projects from ideation through to production, ensuring alignment with business goals and enterprise standards.

• Serve as a key advisor to business leaders, translating complex AI concepts into actionable insights and fostering organization-wide AI adoption.

• Champion responsible AI practices, ensuring ethical, secure, and compliant implementation of AI technologies.

📝 Enhancement Note: This role is positioned at the intersection of AI/ML strategy and operational excellence. While not a traditional Revenue Operations or Sales Operations role, the emphasis on improving "operational performance" and driving "business challenges" through AI solutions strongly aligns with the core objectives of operations functions that leverage technology for efficiency and impact. The candidate will be responsible for the strategic application of AI to optimize processes, enhance decision-making, and drive measurable business outcomes, akin to a senior operations leader focused on technology enablement.

📈 Primary Responsibilities

• Develop and maintain a comprehensive AI/GenAI vision and roadmap, identifying and prioritizing high-impact use cases across various business units in alignment with strategic objectives.

• Evaluate and recommend emerging AI technologies, including LLMs, copilots, multimodal models, and autonomous agents, for practical and prioritized application within the enterprise.

• Establish and enforce standards, reusable design patterns, and best practices for prompt engineering, model tuning, and GenAI solution architecture to ensure consistency and scalability.

• Lead the end-to-end design of AI/ML and GenAI solutions, translating complex business requirements into detailed technical specifications, data flows, and robust architectures.

• Architect and oversee the development of key GenAI components, such as prompt libraries, embedding strategies, vector search mechanisms, knowledge models, and Retrieval-Augmented Generation (RAG) systems.

• Guide critical decisions regarding model selection, fine-tuning, and the integration of AI solutions with existing enterprise systems, ensuring scalability, maintainability, and adherence to security, privacy, and governance standards in partnership with data engineering and IT.

• Manage the AI/GenAI project portfolio, encompassing rigorous scoping, meticulous planning, effective resource allocation, and agile execution to ensure successful delivery.

• Lead and inspire cross-functional teams, including data scientists, engineers, analysts, and business stakeholders, through collaborative agile delivery cycles.

• Act as a trusted advisor to business leaders, providing insights into AI capabilities, potential risks, and best practices, while actively supporting change management initiatives to drive widespread organizational adoption.

• Ensure all AI/GenAI solutions comply with ethical guidelines, fairness principles, privacy regulations, and relevant industry standards, implementing robust content filtering, hallucination mitigation, and usage guardrails.

• Define and enforce acceptable-use policies and risk frameworks for internal GenAI tools, maintaining comprehensive documentation, monitoring systems, and lifecycle management processes.

• Oversee the adoption and governance of enterprise GenAI platforms, such as Microsoft 365 Copilot, Azure OpenAI, and GitHub Copilot, in close collaboration with security and infrastructure teams.

• Develop and deliver comprehensive training programs focused on responsible AI/GenAI usage, and coach teams on effective prompt engineering techniques and human-in-the-loop workflows.

• Define key performance indicators (KPIs) to rigorously measure GenAI performance and overall business value, continuously monitoring for model drift, usage patterns, hallucination rates, and user satisfaction.

• Lead continuous improvement cycles for AI/GenAI systems by leveraging data-driven insights and user feedback to optimize performance and effectiveness.

📝 Enhancement Note: The responsibilities highlight a strong focus on strategic implementation, technical architecture, and leadership within the AI domain. For an operations professional, this translates to understanding how AI can be leveraged to optimize business processes, improve data-driven decision-making, and enhance overall operational efficiency. The emphasis on "operational performance," "scalable solutions," and "measurable business impact" directly maps to core operations objectives.

🎓 Skills & Qualifications

Education:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence/Machine Learning, Engineering, Data Science, or a closely related technical field. Experience:

• A minimum of 7 years of progressive experience in AI/ML, data analytics, or software engineering, with a demonstrated track record of success.

• At least 3 years of experience specifically leading technical teams or architecting and delivering enterprise-scale AI solutions. Required Skills:

• Strong foundational understanding of machine learning principles, large language models (LLMs), Natural Language Processing (NLP), deep learning, and modern automation technologies.

• Hands-on experience with major AI platforms and cloud environments such as Azure ML, Databricks, OpenAI, AWS, or GCP.

• Proven ability to translate complex business needs into scalable, effective technical solutions and architectures.

• Excellent leadership capabilities, with a strong track record of managing technical teams and projects.

• Superior communication, presentation, and stakeholder engagement skills, with the ability to articulate technical concepts to diverse audiences.

• Proficiency in designing and implementing RAG (Retrieval-Augmented Generation) systems and understanding of vector databases.

• Expertise in prompt engineering techniques and best practices for optimizing LLM interactions.

• Experience with MLOps principles and practices for managing the AI lifecycle. Preferred Skills:

• Direct experience deploying enterprise-grade GenAI or LLM-based applications into production environments.

• Relevant cloud certifications (e.g., Azure AI Engineer, AWS Machine Learning Specialty, Google Professional Machine Learning Engineer).

• Previous leadership experience managing hybrid technical and business-facing teams.

• Familiarity with data governance frameworks and responsible AI principles.

• Experience with model tuning and fine-tuning techniques for specific business applications.

📝 Enhancement Note: The qualifications emphasize deep technical expertise in AI/ML and practical experience with enterprise-level deployments. For operations candidates, this means highlighting transferable skills in data analysis, process optimization, technology implementation, and stakeholder management, framed within the context of driving business outcomes through intelligent systems. Experience with data pipelines, system integration, and performance metrics will be highly relevant.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate through case studies how AI/GenAI solutions were designed and implemented to solve specific business problems, leading to measurable improvements in efficiency, productivity, or revenue.

• Showcase examples of architecting scalable AI systems, detailing the technical stack, data flows, and integration points with existing enterprise infrastructure.

• Present evidence of leading cross-functional teams through the development lifecycle of AI projects, highlighting collaboration, problem-solving, and project management methodologies.

• Include documentation or descriptions of how AI solutions were governed, monitored, and continuously improved to ensure ethical use, performance, and ROI. Process Documentation:

• Provide documentation illustrating the process for translating business requirements into technical specifications for AI solutions, including user story mapping and workflow design.

• Showcase examples of creating and managing reusable AI components, such as prompt libraries, embedding strategies, or RAG pipelines, emphasizing standardization and efficiency.

• Present methodologies for defining and tracking KPIs related to AI/GenAI performance, including user adoption, model accuracy, hallucination rates, and business impact.

📝 Enhancement Note: For a role focused on AI solutions design, a portfolio should clearly articulate the candidate's ability to bridge the gap between business needs and technical AI implementation. This includes demonstrating strategic thinking, architectural design, project execution, and the ability to measure and communicate the impact of AI initiatives. Operations professionals can frame their experience in process optimization, system implementation, and data analysis through the lens of AI enablement.

💵 Compensation & Benefits

Salary Range: $160,000 - $180,000 per year

Bonus: 20% bonus based on company performance goals.

Benefits:

• Comprehensive Medical, Dental, and Vision Insurance plans.

• 401(k) retirement savings plan with a company match.

• Company-paid Life and Disability Insurance coverage.

• Opportunities for professional development, including training, certifications, and conferences.

Working Hours: Standard full-time hours, likely around 40 hours per week, with flexibility expected for project demands and team collaboration in a hybrid environment.

📝 Enhancement Note: The provided salary range of $160,000-$180,000 is competitive for a managerial role focused on advanced technology solutions in the Rochester, NY area, reflecting the specialized skills and experience required. The 20% bonus structure incentivizes company-wide performance, aligning individual success with organizational objectives. The benefits package is standard for a professional role, with a notable emphasis on professional development, which is crucial for staying current in the rapidly evolving AI field.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology / AI Solutions / Professional Services

Company Size: Mindex is likely a mid-sized to large organization based on the scope of this role and the need for enterprise-wide AI solutions. The company's focus on AI suggests a forward-thinking, innovation-driven culture.

Founded: The founding date of Mindex is not provided, but the company's engagement with advanced AI technologies implies a commitment to modern business practices and technological advancement.

Team Structure:

• The AI Solutions Design Manager will likely lead a dedicated AI/GenAI team, potentially comprising data scientists, ML engineers, prompt engineers, and solution architects.

• This role requires close collaboration with various business units, IT departments, data engineering teams, and potentially product management.

• Reporting lines are expected to be within a technology or innovation leadership structure, potentially reporting to a CTO, VP of Technology, or Head of AI/Data Science. Methodology:

• Data-driven decision-making is paramount, with a strong emphasis on measuring the impact and ROI of AI initiatives.

• Agile methodologies are expected for project delivery, allowing for iterative development, rapid prototyping, and continuous improvement.

• A focus on responsible AI and ethical implementation will guide the design and deployment of all solutions.

• Collaboration and knowledge sharing across teams will be essential for fostering innovation and accelerating adoption.

Company Website: https://mindex.com/

📝 Enhancement Note: The company's engagement with AI solutions suggests an environment that values innovation, technical expertise, and strategic thinking. For operations professionals, understanding how AI integrates into business processes and drives efficiency will be key to aligning with Mindex's operational and technological philosophy.

📈 Career & Growth Analysis

Operations Career Level: This role is at a senior management level, focusing on strategic AI implementation that directly impacts operational performance and business outcomes. It represents a significant step for individuals looking to specialize in AI leadership within a business context.

Reporting Structure: The AI Solutions Design Manager will likely report to a senior technology executive (e.g., CTO, VP of Engineering, Head of Data Science) and will be responsible for leading a team of AI specialists and collaborating extensively with business stakeholders across different departments.

Operations Impact: The role's core function is to drive business impact through the strategic design and delivery of AI/GenAI solutions. This includes improving operational efficiency, unlocking new business opportunities, enhancing customer experiences, and potentially contributing to revenue growth through AI-enabled products or services.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific AI domains like LLMs, autonomous agents, or multimodal AI, becoming a recognized subject matter expert.

• Leadership Advancement: Progress to Director or VP-level roles within AI/Data Science or Technology leadership, overseeing broader AI initiatives and strategy.

• Cross-Functional Leadership: Transition into broader operational leadership roles where AI expertise is leveraged to drive digital transformation across multiple business functions.

• Industry Influence: Contribute to the AI community through speaking engagements, publications, or open-source contributions.

📝 Enhancement Note: This role offers a significant growth trajectory for individuals passionate about AI and its application in business. It provides opportunities to lead cutting-edge initiatives, develop advanced technical and leadership skills, and make a tangible impact on an organization's strategic direction and operational efficiency.

🌐 Work Environment

Office Type: Hybrid work environment, combining remote work flexibility with in-office collaboration.

Office Location(s): Rochester, New York, United States. Specific details on office amenities and collaborative spaces are not provided but can be inferred to support hybrid work models.

Workspace Context:

• The workspace will likely accommodate both focused individual work (remote) and collaborative team sessions, brainstorming, and stakeholder meetings (in-office).

• Access to modern technology and tools is expected, including high-performance computing resources, relevant software licenses, and robust network connectivity.

• Opportunities for interaction with cross-functional teams, including data scientists, engineers, business analysts, and leadership, will be integral to the role.

Work Schedule: A standard full-time work schedule (approximately 40 hours per week) is expected, with inherent flexibility to manage project timelines and attend to urgent operational needs related to AI deployments. The hybrid model allows for a balance between personal work preferences and collaborative requirements.

📝 Enhancement Note: The hybrid model is designed to offer flexibility while ensuring sufficient in-person collaboration for complex problem-solving and team building, crucial for a role involving strategic AI design and cross-functional leadership.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and application to assess alignment with the core requirements for AI/GenAI expertise and leadership experience.

• Technical Interview(s): In-depth discussions focusing on your understanding of machine learning, LLMs, solution architecture, RAG systems, prompt engineering, and experience with relevant AI platforms. Expect scenario-based questions and problem-solving exercises.

• Portfolio Presentation: A session where you will present relevant case studies from your past work, detailing your approach to designing and implementing AI solutions, the challenges you faced, and the measurable business impact achieved.

• Leadership & Strategy Interview: An assessment of your leadership style, strategic thinking, ability to manage cross-functional teams, and experience in advising business stakeholders on AI adoption and governance.

• Final Round/Executive Interview: A discussion with senior leadership to assess cultural fit, strategic alignment, and overall suitability for the role and Mindex's vision.

Portfolio Review Tips:

• Quantify Impact: For each case study, clearly articulate the business problem, your proposed AI solution, the technical details of its implementation, and most importantly, the measurable business outcomes (e.g., efficiency gains, cost savings, revenue increase, improved accuracy).

• Showcase Architecture: Detail the architectural design of your AI solutions, including data pipelines, model selection rationale, integration points, and scalability considerations. Use diagrams where appropriate.

• Highlight Leadership: Describe your role in leading teams, managing projects, and collaborating with diverse stakeholders (technical and non-technical). Emphasize your ability to translate complex AI concepts.

• Address Governance: Include examples of how you've incorporated responsible AI principles, ethical considerations, and governance frameworks into your solution designs and deployments.

Challenge Preparation:

• Be prepared for technical challenges that may involve designing an AI solution for a hypothetical business problem, outlining prompt engineering strategies, or discussing model selection criteria.

• Practice articulating your thought process clearly and concisely, demonstrating your ability to think critically and solve complex problems under pressure.

• Prepare to discuss your approach to managing AI project risks, ensuring data privacy, and driving user adoption of new AI technologies.

📝 Enhancement Note: The interview process emphasizes a blend of technical acumen, leadership capability, and strategic vision. For candidates with operations backgrounds, highlighting experience in process optimization, data analysis, project management, and stakeholder communication, framed within the context of AI's potential impact, will be crucial. A strong portfolio demonstrating tangible results from technology-driven initiatives is essential.

🛠 Tools & Technology Stack

Primary Tools:

• AI/ML Platforms: Azure ML, Databricks, OpenAI (Azure OpenAI Service), AWS SageMaker, Google Cloud AI Platform.

• LLM/GenAI Frameworks: Experience with foundational LLMs and frameworks for building GenAI applications (e.g., LangChain, LlamaIndex).

• Cloud Computing: Deep familiarity with at least one major cloud provider (Azure, AWS, GCP) and their AI/ML services.

• Data Science & ML Libraries: Python (Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy), R.

Analytics & Reporting:

• BI Tools: Tableau, Power BI, or similar for visualizing AI performance metrics and business impact.

• Monitoring Tools: Solutions for tracking model performance, usage patterns, and system health (e.g., Prometheus, Grafana, cloud-native monitoring services).

CRM & Automation:

• Integration Tools: Experience with APIs and middleware for integrating AI solutions with existing enterprise systems (CRM, ERP, etc.).

• Workflow Automation Platforms: Understanding of how AI can be integrated into broader automation workflows.

• Version Control: Git and associated platforms (e.g., GitHub, GitLab, Azure DevOps).

📝 Enhancement Note: Proficiency with a range of AI/ML platforms, cloud services, and data science libraries is a core requirement. For operations professionals, demonstrating familiarity with how these tools integrate with business systems (like CRMs) and impact operational workflows will be a significant advantage.

👥 Team Culture & Values

Operations Values:

• Innovation & Forward-Thinking: A commitment to exploring and implementing cutting-edge AI technologies to drive business advantage and operational excellence.

• Data-Driven Decision Making: Reliance on empirical evidence, metrics, and analytics to guide AI strategy, solution design, and performance evaluation.

• Collaboration & Cross-Functional Partnership: Strong emphasis on working effectively with diverse teams across the organization to achieve shared goals.

• Responsible & Ethical AI: Dedication to developing and deploying AI solutions that are fair, transparent, secure, and aligned with ethical principles and regulatory compliance.

• Continuous Improvement: A proactive approach to refining AI models, processes, and solutions based on feedback, performance data, and evolving business needs.

Collaboration Style:

• Agile & Iterative: Embracing flexible methodologies that allow for rapid prototyping, feedback loops, and continuous adaptation.

• Transparent Communication: Openly sharing insights, progress, and challenges with stakeholders at all levels.

• Empowerment & Ownership: Fostering an environment where team members are empowered to take ownership of their work and contribute innovative ideas.

• Knowledge Sharing: Encouraging the dissemination of best practices, learnings, and technical expertise across teams to build collective intelligence.

📝 Enhancement Note: The culture likely values technical expertise, strategic thinking, and a collaborative spirit. Operations professionals will find common ground in the emphasis on data-driven results, process improvement, and cross-functional teamwork, applied within the advanced domain of AI.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Staying abreast of the fast-paced advancements in AI technologies and identifying the most relevant and impactful applications for the business.

• Ensuring Responsible AI Implementation: Navigating the complexities of ethical considerations, bias mitigation, data privacy, and regulatory compliance in AI deployments.

• Driving Organizational Adoption: Overcoming resistance to change and ensuring effective adoption of new AI tools and processes across diverse user groups.

• Measuring ROI and Business Impact: Clearly defining and quantifying the value generated by AI initiatives, particularly for complex or indirect benefits.

• Talent Acquisition & Retention: Attracting and retaining top AI talent in a highly competitive market.

Learning & Development Opportunities:

• Cutting-Edge AI Research: Access to resources and opportunities to explore and experiment with the latest AI models, techniques, and platforms.

• Advanced Training & Certifications: Support for pursuing specialized certifications in AI, cloud computing, and related technologies.

• Industry Conferences & Networking: Opportunities to attend leading AI conferences, share insights, and build professional networks.

• Leadership Development: Mentorship and training programs focused on enhancing leadership skills in managing technical teams and driving strategic initiatives.

📝 Enhancement Note: This role presents significant intellectual challenges and opportunities for continuous learning. The ability to adapt to new technologies and translate them into practical business solutions will be key to success and professional growth.

💡 Interview Preparation

Strategy Questions:

• "How would you develop an AI strategy for a company in our industry, and what key metrics would you track to ensure its success?" (Prepare to discuss industry analysis, stakeholder alignment, use case prioritization, and KPI frameworks.)

• "Describe a time you had to translate a complex business requirement into a technical AI solution. What was your process, and what challenges did you overcome?" (Focus on your problem-solving methodology, architectural design, and stakeholder communication.)

• "Given the rapid pace of AI development, how do you stay current with new technologies and trends, and how would you evaluate which ones are relevant for our business?" (Highlight your learning strategies, critical evaluation skills, and alignment with business objectives.) Company & Culture Questions:

• "What are your thoughts on responsible AI, and how would you ensure ethical considerations are embedded in our AI solutions?" (Prepare to discuss fairness, bias, privacy, transparency, and governance frameworks.)

• "How would you foster collaboration between your AI team and our business units to drive adoption and maximize the impact of AI solutions?" (Emphasize communication, empathy, change management, and user enablement strategies.)

• "What do you see as the biggest opportunities and risks of integrating Generative AI into enterprise operations?" (Demonstrate strategic foresight and risk management capabilities.) Portfolio Presentation Strategy:

• Structure Your Narrative: For each case study, clearly define the problem, your solution, your specific contributions, the technology stack used, and the quantifiable business impact. Use a STAR (Situation, Task, Action, Result) or similar framework.

• Visualize Your Work: Use diagrams to illustrate system architecture, data flows, and workflow optimizations. Showcase dashboards or reports if applicable to demonstrate impact.

• Focus on Impact: Quantify results wherever possible. Use percentages, dollar figures, or time savings to demonstrate the ROI of your AI solutions.

• Be Ready for Deep Dives: Anticipate detailed questions about your technical decisions, architectural choices, and problem-solving approaches.

📝 Enhancement Note: Prepare to articulate your strategic vision for AI, your technical expertise, your leadership capabilities, and your understanding of responsible AI practices. Demonstrating a clear link between AI initiatives and tangible business outcomes will be critical.

📌 Application Steps

To apply for this AI Solutions Design Manager position:

• Submit your application through the Mindex careers portal or the provided link.

• Tailor Your Resume: Emphasize your experience in AI/ML strategy, solution architecture, Generative AI, LLMs, prompt engineering, and managing technical teams. Quantify achievements with specific metrics where possible.

• Curate Your Portfolio: Select 2-3 strong case studies that best showcase your ability to design and deliver impactful AI solutions. Focus on clarity, technical detail, and measurable business outcomes. Be prepared to walk through these in detail.

• Research Mindex: Understand the company's mission, industry, and any publicly available information on their technology initiatives. Consider how your AI expertise can align with their strategic goals.

• Prepare for Technical & Behavioral Questions: Practice articulating your technical knowledge, leadership approach, and problem-solving skills, particularly in the context of AI strategy and implementation.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.