Head of AI / Chief Technology Officer (CTO)

The Head of AI / CTO will be responsible for defining and executing the company’s technology vision, building its AI engineering capabilities, developing innovative AI solutions, establishing strategic technology partnerships, and leading the technical delivery of customer engagements.

This individual will play a critical role in shaping the company’s service portfolio, building the technical team, supporting business development, and creating repeatable AI solutions that can be deployed across multiple industries.

The ideal candidate combines deep hands-on AI engineering expertise with enterprise architecture, consulting leadership, and a strong understanding of how to translate AI technologies into tangible business value.

As a founding technology leader, the successful candidate must be comfortable operating in a startup environment, making architectural decisions, developing prototypes, engaging directly with customers, and building the organization as the business grows., 1. AI Strategy and Technology Leadership

• Define and execute the company’s AI technology strategy and long-term technical roadmap.
• Establish a scalable, vendor-neutral AI technology architecture.
• Evaluate emerging AI technologies, models, platforms, and development frameworks.
• Identify commercially viable AI solutions and consulting service offerings.
• Collaborate with the CEO to define technology investments, service offerings, and growth priorities.
• Establish technical standards, development methodologies, and engineering best practices.
• Balance innovation with customer value, security, reliability, and profitability.

1. Generative AI, LLMs, and Agentic AI Development

• Lead the architecture, development, and deployment of enterprise-grade AI applications.
• Design and implement solutions using leading foundation models and LLM platforms.
• Develop AI agents capable of performing multistep business workflows using approved tools and integrations.
• Architect Retrieval-Augmented Generation (RAG) solutions for enterprise knowledge management.
• Implement document intelligence, conversational AI, intelligent search, and AI-powered automation.
• Design model orchestration, tool calling, structured outputs, and human-in-the-loop workflows.
• Evaluate model accuracy, reliability, latency, performance, and operational cost.
• Establish reusable frameworks and accelerators to reduce implementation time., * Integrate AI applications with CRM, ERP, HR, financial, collaboration, and custom enterprise systems.
• Design cloud-native AI architectures using Microsoft Azure, AWS, and Google Cloud.
• Establish standards for API integration, data pipelines, vector databases, and knowledge retrieval.
• Implement authentication, authorization, access controls, encryption, logging, and monitoring.
• Evaluate proprietary, open-weight, and third-party models based on business and technical requirements.
• Design systems that support appropriate model portability and minimize unnecessary vendor lock-in.

1. AI Consulting and Customer Delivery

• Serve as the senior technical adviser for strategic customer engagements.
• Lead AI discovery workshops, solution architecture sessions, and technical assessments.
• Translate business challenges into practical AI solutions and implementation roadmaps.
• Partner with business consultants to develop AI business cases, feasibility assessments, and ROI estimates.
• Oversee proofs of value, pilot projects, production implementations, and managed AI services.
• Establish delivery quality standards, technical acceptance criteria, and project governance.
• Participate in executive-level customer presentations and solution demonstrations.
• Resolve complex technical issues and provide architectural oversight for critical engagements.

1. Build and Lead the AI Engineering Organization

• Recruit, mentor, and lead a high-performing team of AI engineers, solution architects, data engineers, and developers.
• Define the organizational structure, technical roles, career paths, and hiring priorities.
• Establish an internal AI training and technical development program.
• Build reusable engineering assets, coding standards, and knowledge-sharing practices.
• Manage internal resources and qualified external contractors.
• Establish engineering productivity, quality, and delivery performance metrics.
• Create a culture of innovation, accountability, collaboration, and continuous learning.

1. AI Governance, Security, and Responsible AI

• Establish an enterprise AI governance and risk management framework.
• Implement controls addressing data privacy, information security, model reliability, and responsible AI.
• Develop testing and evaluation methodologies for hallucinations, prompt injection, data leakage, and unsafe agent behavior.
• Ensure appropriate human approval for high-impact or consequential automated actions.
• Establish auditability, observability, incident response, and production monitoring.
• Support customer security reviews, vendor risk assessments, and compliance requirements.
• Align technical practices with relevant frameworks, including the NIST AI Risk Management Framework and applicable data protection requirements.

1. Technology Partnerships and Ecosystem Development

• Build technical relationships with major AI and cloud technology providers.
• Evaluate partnerships with OpenAI, Microsoft, AWS, Google Cloud, Anthropic, and other relevant providers.
• Identify opportunities for technical certifications, partner programs, and co-selling relationships.
• Maintain awareness of emerging AI technologies and competitive developments.
• Develop reference architectures, demonstrations, and reusable accelerators that strengthen the company’s market position.

1. Business Development and Commercial Leadership

• Partner with the CEO and sales leadership to develop the company’s AI consulting go-to-market strategy.
• Participate in customer discovery meetings, technical presentations, and executive workshops.
• Lead technical presales activities, solution demonstrations, and proposal development.
• Estimate implementation effort, technical costs, delivery resources, and project risks.
• Support pricing decisions and ensure proposed solutions can be delivered profitably.
• Identify opportunities for managed services, recurring revenue, and expansion within customer accounts.
• Help establish the company’s reputation as a trusted enterprise AI transformation partner.

• 10+ years of progressive experience in software engineering, enterprise architecture, cloud technologies, or technology consulting.
• 3+ years of substantial hands-on experience developing and deploying AI/ML or Generative AI solutions, including recent production LLM implementations.
• Demonstrated experience architecting enterprise-grade AI applications.
• Proven ability to lead technical teams and deliver complex technology projects.
• Experience working in a technology consulting, professional services, systems integration, or enterprise software organization.
• Experience engaging directly with senior business and technology executives.
• Demonstrated ability to translate business requirements into scalable technical solutions.
• Experience establishing technical standards, engineering practices, and delivery methodologies.

Technical Expertise

Generative AI and LLMs

• OpenAI APIs, Anthropic Claude, Google Gemini, and/or comparable foundation models
• Prompt engineering, structured generation, tool calling, and model orchestration
• Retrieval-Augmented Generation (RAG)
• Agentic AI and multi-step workflow automation
• LLM evaluation, monitoring, and optimization
• Fine-tuning and model customization where appropriate

AI Frameworks and Engineering

• Python and modern backend development
• LangChain, LangGraph, Semantic Kernel, LlamaIndex, or comparable frameworks
• REST APIs, microservices, and integration architecture
• Vector databases and semantic search
• SQL and modern data engineering concepts
• Git, CI/CD, automated testing, and infrastructure automation

Cloud and Infrastructure

• AWS AI services, including Amazon Bedrock
• Docker, Kubernetes, and containerized deployments
• Cloud security, identity management, and observability
• Scalable production deployment and operational support

Candidates are not expected to have expert-level experience with every listed platform, but must demonstrate strong architectural judgment and depth in at least one major enterprise cloud ecosystem., * Previous experience as a CTO, Head of AI, VP of Engineering, Principal AI Architect, or AI Practice Leader.

• Experience building an AI consulting practice, startup, or technology services business.
• Experience creating reusable AI accelerators or commercial AI products.
• Strong background in professional services delivery economics and resource planning.
• Experience establishing enterprise AI governance frameworks.
• Experience building strategic cloud or technology partnerships.
• Demonstrated success converting AI prototypes into reliable production solutions.
• Experience working with US mid-market or enterprise customers.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field, or equivalent practical experience.

We are launching an independent, next-generation AI consulting and technology services company focused on helping mid-market and enterprise organizations transform their businesses through Artificial Intelligence.

Our mission is to help organizations move beyond AI experimentation and implement practical, secure, scalable AI solutions that deliver measurable business outcomes.

We will provide end-to-end AI consulting services, including:

• Enterprise AI Strategy and Transformation
• Generative AI and Large Language Model (LLM) Solutions
• Agentic AI and Intelligent Workflow Automation
• Custom AI Application Development
• Enterprise AI Integration and Modernization
• AI Governance, Security, and Risk Management
• Managed AI Services and Continuous Optimization