Title: Managing Director, AI Portfolio Solutions 

Job Summary 

The Managing Director, AI Portfolio Solutions is responsible for defining, evolving, and governing the organization’s Artificial Intelligence (AI) and Data portfolio, solution patterns, and service offerings aligned to customer outcomes and market demand.  This role bridges strategy and execution, partnering closely with Solution Architects, Professional Services, Sales, and OEM (Original Equipment Manufacturer) / ISV (Independent Software Vendor) partners to create scalable, repeatable, and profitable AI-driven offerings that integrate hardware, software, and services.
 
This individual owns the AI practice’s technical go-to-market (GTM) strategy, drives portfolio rationalization, and ensures alignment between customer imperatives, emerging AI technologies (including generative AI, machine learning, and data platforms), and business objectives. The role carries accountability for revenue growth and gross profit (GP) attainment, translating market insights into differentiated AI solutions that resonate from CxO priorities through technical implementation. 

Responsibilities

1. AI Discipline Strategy & Technical Go-to-Market

• Define and maintain the AI practice strategy and technical GTM plan aligned with corporate, engineering, and services objectives.
• Translate market demand, customer AI adoption maturity, and OEM/ISV roadmaps into a prioritized portfolio of AI solutions, platforms, and services.
• Establish clear value propositions and differentiated use cases across AI domains such as:
  • Generative AI and LLM-based applications.
  • Predictive analytics and machine learning.
  • AI infrastructure (GPU, edge AI, high-performance compute).
  • Data platforms and AI-ready architectures.
• Partner with Sales and Marketing to develop AI-focused sales plays, messaging, and positioning that link AI capabilities to measurable business outcomes.

 2. AI Portfolio & Offer Management

• Own the technical definition and lifecycle of the AI portfolio, including:
  • Standardized AI solution architectures and patterns.
  • Productized AI Professional Services (assessments, pilots, implementations).
  • Managed AI services (model operations, monitoring, optimization).
  • AI infrastructure offerings (compute, storage, networking for AI workloads).
  • Supporting assets such as reference designs, BOMs (including GPU/accelerated hardware), deployment models, and Machine Learning Operations (MLOps) / Large Language Model Operations (LLMOps) runbooks.
• Ensure AI offerings are scalable, repeatable, margin-aware, and executable with clear delivery frameworks.
• Continuously refine the portfolio based on win/loss insights, model performance outcomes, delivery feedback, utilization, and customer success metrics. 

3. AI Reference Architecture & Solution Patterns

• Define and govern AI reference architectures, blueprints, and design standards aligned with enterprise security, compliance, and responsible AI principles.
• Ensure architectures address key non-functional requirements including scalability, performance (especially for GPU workloads), data governance, model lifecycle management, observability, and security.
• Align AI architectures with cross-domain capabilities including data pipelines, cloud/hybrid infrastructure, networking, identity, and automation.
• Provide technical oversight on complex AI opportunities, ensuring feasibility, cost-efficiency, and alignment with best practices (e.g., MLOps, LLMOps, data engineering). 

4. Vertical AI Solutions & Industry Use Cases

• Develop AI-driven solutions tailored to priority industries, including:
  • Financial Services: fraud detection, risk modeling, regulatory compliance, AI-driven customer insights.
  • Retail: personalization, demand forecasting, supply chain optimization, computer vision for stores.
  • Entertainment, Gaming & Hospitality: real-time recommendations, content personalization, guest experience AI.
  • Media & Entertainment: content generation, tagging, search, and streaming optimization. o Healthcare: clinical decision support, imaging AI, data privacy, and compliance-driven AI solutions.
  • Manufacturing (including Semiconductor): predictive maintenance, quality/yield optimization, computer vision for inspection.
  • Construction and Energy: asset intelligence, field AI, predictive maintenance, remote operations.
• Create AI reference architectures, data models, and implementation frameworks aligned to industry-specific requirements and buying centers.
• Clearly articulate business value, risk mitigation (including AI governance), and TCO/ROI for AI investments. 

5. Market, Competitive & OEM/ISV AI Strategy

• Maintain a current perspective on:
  • AI market trends (e.g., generative AI adoption, foundation models, edge AI).
  • Leading and emerging AI OEMs/ISVs (cloud AI platforms, model providers, data platforms, AI infrastructure vendors).
  • Competitive AI solution approaches and pricing models.
• Evaluate, select, and rationalize strategic AI technology partners (e.g., hyperscalers, GPU vendors, AI software platforms) in collaboration with Alliances and executive leadership.
• Define partner roles within the AI portfolio (primary, secondary, niche) and ensure clear differentiation of the organization’s value-added services (integration, optimization, lifecycle management). 

6. Financial   Accountability

• Revenue and Gross Profit Responsibilities
  • Develop and manage annual forecasts, and long-term financial plans.
  • Monitor financial performance against targets and implement corrective actions to improve profitability.
  • Analyze revenue streams, pricing strategies, and cost structures to optimize margins.
  • Drive expense management initiatives and identify cost-saving opportunities without compromising quality.
• Strategic & Analytical
  • Provide financial insights and recommendations to support strategic decision-making.
  • Conduct variance analysis (actual vs. budget/forecast) and present findings to senior leadership.
  • Evaluate new business opportunities, investments, and ROI scenarios.
  • Partner with cross-functional teams (sales, operations, marketing) to align financial goals. 

Technical & Professional Skills

• Strong ability to see the “big picture” across AI, data, applications, and infrastructure domains.
• Deep understanding of AI/ML technologies, including generative AI, model lifecycle management, and data engineering concepts.
• Strong knowledge of AI infrastructure, including GPU-based compute, storage, networking, and hybrid/cloud architectures.
• Familiarity with leading AI platforms, frameworks, and ecosystems (e.g., hyperscalers, model providers, data/AI tooling).
• Proven ability to translate executive-level AI strategies into actionable, scalable solutions.
• Strong strategic thinking and analytical skills, with the ability to synthesize AI market trends and translate them into a clear roadmap.
• Solid financial acumen, including P&L ownership, ROI modeling, and business case development for AI investments.
• Proven experience building or managing AI or data-focused portfolios, practices, or solution offerings.
• Excellent communication and presentation skills, with the ability to engage both executive stakeholders and technical teams.
• Ability to influence cross-functional teams without direct authority in a matrixed organization.

 Education & Experience

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field (Master’s preferred).
• 10+ years of experience in technology strategy, AI/data solutions architecture, or practice leadership.
• Hands-on or leadership experience in AI/ML, data platforms, or advanced analytics initiatives.
• Experience working within or alongside Professional Services and Managed Services organizations.
• Proven track record of driving revenue growth and profitability in technology or AI practice.

 
Language Skills:

• Proficient in English (verbal and written communication).
• Effective communication and people skills.

 
Physical Requirements: 

• Prolonged periods of sitting at a workspace and working on a computer. 
• Must be able to lift, in an upward motion, a minimum of 45 pounds.

 
Travel Requirements:

• Requires driving and flying locally and overnight to Customer, Vendor, and Company locations  and events. 
• This is an in-office position for those employees who live within 35 miles of a Technologent office. 

Technologent is an Equal Opportunity Employer -- EEO/AA Employer/Vet/Disabled -- for reasonable accommodations, please contact us at hr@technologent.com
 
Technologent is a Global Provider of Edge-to-Edge℠ Information Technology Solutions and Services for Fortune 1000 and SMB companies. We offer a unique blend of business practices that are aligned to solve for top CIO concerns. Our core competencies focus on data center infrastructure, business continuity, data protection, service automation and orchestration, continuous intelligence, monitoring, connectivity, collaboration and cybersecurity. These practices are supported by our professional services, digital transformation services and financial services offerings. By providing custom solutions and services designed to fit your business needs, we enable your organization to be more agile, responsive and competitive. Technologent empowers your company to ascend to the next level in IT.

Headquartered in Irvine, CA, Technologent has offices throughout the US and proudly serves clients around the world. When partnering with Technologent, organizations benefit from the highest caliber of professionals, committed to delivering exceptional business outcomes backed by unmatched service and support.