# Jobgether — Vice President of Technology - AI - Generated: 2026-09-04 11:30:38 AM EDT - Original/canonical posting: https://www.linkedin.com/jobs/view/vice-president-of-technology-ai-at-jobgether-4460453601 - LinkedIn job ID: 4460453601 - Provider: LinkedIn - Posting company: Jobgether, on behalf of an undisclosed partner company - Title: Vice President of Technology - AI - Employment type / listed seniority: Full-time / Director - Work model/location: Remote work opportunity within the United States; LinkedIn location is United States - Posting age at retrieval: 1 day ago (exact timestamp not publicly exposed; approximately one day elapsed) - Applicants at retrieval: 109 applicants - Compensation: $210,000-$270,000 base salary plus a 33% bonus opportunity - Travel: Not disclosed - Positioning track: Executive leader (hands-on consultative AI architecture and delivery) - Capability fit score before hard-gap decision: 94.0% - Fit outcome: FAIL ## Central mandate Lead the architecture and end-to-end delivery of advanced AI solutions for consumer-products and retail clients. Combine hands-on software and model implementation with client discovery, AI strategy, roadmaps, project estimation, delivery management, multidisciplinary-team mentoring, enterprise integration, and responsible-AI practices. ## Fit decision FAIL. The live LinkedIn page displays **109 applicants**, exceeding Keith's standing ceiling of 90. This is an operational hard gate, so no resume, cover letter, or LinkedIn connection note was generated without an explicit override. Before the hard-gap decision, Keith's source-supported capabilities cover 94.0% of the weighted requirements. ## Weighted evidence map | Requirement | Weight | Evidence score | Source-supported evidence | |---|---:|---:|---| | Design scalable AI/ML architectures across GenAI, LLMs, NLP, computer vision, and MLOps | 3 | 3 | Architected and built the production AssistX GenAI platform; extensive applied-ML, SageMaker, TensorFlow, MLOps, NLP/NLU, Rekognition, distributed-system, and AI-platform work. | | Use modern AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, or comparable tools | 3 | 2 | Direct TensorFlow, LangChain, LangGraph, SageMaker, RAG, agentic-AI, and model-evaluation evidence; PyTorch and Hugging Face are not documented. | | Lead hands-on coding, prototyping, implementation, pipelines, and deployment, with strong Python skills | 3 | 3 | Built the AssistX core platform and most of its initial HR assistant hands-on using Python, FastAPI, LangChain, LangGraph, PostgreSQL/PGVector, AWS, APIs, and infrastructure as code. | | Design and deploy AI/ML solutions on cloud AI platforms | 3 | 3 | Four years in AWS specialist roles plus hands-on SageMaker, TensorFlow, Vertex AI, AWS AI services, cloud architecture, deployment, and operations. | | Oversee data engineering, model training/evaluation/optimization, and enterprise integration | 3 | 3 | Delivered enterprise data lakes, ETL/data pipelines, forecasting and optimization systems, model governance/evaluation, and integrations across 11 ERP systems and major CCaaS platforms. | | Run consultative client discovery and translate business problems into AI strategies and roadmaps | 3 | 3 | Advised 200+ AWS enterprises, worked directly with VP/C-level and technical stakeholders, ran workshops, and converted requirements into funded roadmaps and production systems. | | Scope, estimate, plan, budget, and deliver complex AI initiatives end to end | 3 | 3 | Direct estimating, planning, scheduling, cost/risk management, delivery, and budget ownership; shipped AssistX in eight months, SuccessKPI's first platform in six months, and TriMark's data lake in seven months. | | Lead, mentor, and develop multidisciplinary technical teams | 3 | 3 | Recruited and directly led 11 IDW engineers, led seven-person and 24-person distributed teams, managed managers, and built AWS technical communities of 80-100 specialists. | | Apply responsible AI, governance, ethics, privacy, security, and risk management | 2 | 3 | Led MassMutual AI-governance and privacy architecture covering transparency, bias, drift, auditability, CCPA, CPRA, GDPR, security, and policy automation. | | Bachelor's or master's degree in Computer Science, Engineering, Data Science, or a related technical discipline | 2 | 2 | Ph.D. in Molecular Biology / Microbiology & Immunology plus decades of software, cloud, data, and AI engineering; degree title is not CS, engineering, or data science. | Weighted result: 79 of 84 possible points = 94.0%. ## Direct-match strengths - Unusually strong combination of executive AI leadership, hands-on architecture/coding, client consulting, and production delivery. - Direct GenAI, LLM, RAG, agents, Python, TensorFlow, LangChain/LangGraph, SageMaker, MLOps, data engineering, integration, governance, and cloud-platform evidence. - Quantified outcomes include 70+ AI automations across 20+ workflows, an eight-month commercial release, up to 50x workflow acceleration, a 60x reporting improvement, and 200+ AWS enterprises advised. - Relevant consulting background includes building NorthBay's AI/ML practice, delivering customer AI/data solutions, and guiding enterprise technology adoption at AWS. ## Hard and material gaps - Hard constraint: LinkedIn displays 109 applicants; Keith's ceiling requires skipping any role whose JD page displays 90 or more applicants. - PyTorch and Hugging Face are not documented in Keith's source materials, although the posting allows comparable platforms and Keith has direct TensorFlow, LangChain, and LangGraph evidence. - Recent end-to-end computer-vision model ownership is not documented; Keith has Amazon Rekognition, video-analysis advisory, and 3D imaging-platform evidence. - The qualifying technical degree is a Ph.D. in a scientific discipline rather than the specifically named CS, engineering, or data-science fields. - Spanish or Portuguese is not documented, but the posting lists it only as a plus. - Travel is undisclosed and is not treated as a fit failure. ## Keyword diagnostic Strong source-supported alignment: AI/ML architecture, generative AI, LLMs, NLP, machine learning, MLOps, Python, TensorFlow, LangChain, SageMaker, AWS, Vertex AI, data engineering, model evaluation, enterprise integration, client discovery, AI strategy, roadmaps, project estimation, delivery planning, multidisciplinary-team mentoring, AI governance, responsible AI, privacy, security, and risk management. Weak or unsupported exact terms: PyTorch, Hugging Face, Azure ML, Spanish, and Portuguese. These were not added as candidate claims. ## Artifact decision and metadata - Archived JD capture: https://files.keithsteward.com/Jobgether/Keith_Steward_Vice_President_of_Technology_AI_4460453601_JD_capture.md - Resume: not generated because the fit gate returned FAIL and no override was supplied. - Cover letter: not generated because the fit gate returned FAIL and no override was supplied. - LinkedIn connection note: not generated because the skill prohibits one for FAIL roles. - Google Drive used: no. ## Full normalized job description This position is listed on behalf of a partner company, which manages all applications and next steps. The partner is looking for a Vice President of Technology - AI based in the United States. The Vice President of Technology - AI will lead the architecture and delivery of advanced artificial intelligence solutions for clients across consumer products and retail. This is a hands-on, consultative leadership role combining deep technical expertise with strategic client engagement. The role will design scalable AI and machine-learning architectures while guiding projects from discovery and estimation through implementation and deployment. It offers work with emerging technologies across generative AI, LLMs, computer vision, NLP, and MLOps. The role partners with business and technical stakeholders to define AI strategies, identify high-value use cases, build practical adoption roadmaps, mentor multidisciplinary teams, and shape innovative solutions. ### Accountabilities - Design robust, scalable AI and ML architectures tailored to complex client requirements, leveraging generative AI, large language models, NLP, computer vision, and modern MLOps practices. - Evaluate and select appropriate AI models, frameworks, technologies, and cloud platforms across AWS, Azure, and GCP based on business requirements and technical considerations. - Lead hands-on development through coding, prototyping, model implementation, pipeline development, and deployment, setting a strong technical example for delivery teams. - Oversee data engineering, model training, evaluation, optimization, and integration of AI solutions with enterprise systems. - Engage directly with clients to understand business challenges, facilitate technical and discovery workshops, and translate business needs into scalable AI solutions. - Advise clients on AI strategies, technology roadmaps, adoption approaches, and best practices for deploying and scaling AI capabilities. - Scope and estimate AI initiatives, including project requirements, resource needs, timelines, costs, dependencies, and delivery milestones. - Develop and manage delivery plans, ensuring AI projects are delivered on time, within budget, and according to quality and performance expectations. - Lead end-to-end project delivery while maintaining strong client relationships and ensuring successful business outcomes. - Mentor and guide cross-functional teams including data scientists, software engineers, analysts, and other technical specialists. - Monitor emerging AI research, models, platforms, frameworks, and industry developments, evaluating new technologies and piloting those with potential business value. - Promote responsible AI practices, including governance, ethics, privacy, security, and appropriate risk management across solution design and implementation. ### Requirements - Bachelor's or master's degree in Computer Science, Engineering, Data Science, or a related technical discipline. - 7+ years of software-engineering experience, including at least 3 years focused on AI/ML solution architecture. - Proven hands-on experience with modern AI/ML frameworks and technologies such as TensorFlow, PyTorch, Hugging Face, LangChain, or comparable platforms. - Deep technical knowledge of large language models, generative AI, NLP, computer vision, machine learning, and MLOps. - Strong programming skills in Python; experience with Java, Scala, or R is a plus. - Experience designing and deploying AI/ML solutions using cloud platforms and services such as AWS SageMaker, Azure ML, or GCP Vertex AI. - Demonstrated ability to scope, estimate, plan, and successfully deliver complex AI and technology projects. - Strong consultative skills, with the ability to translate complex technical concepts into clear business recommendations for both technical and executive stakeholders. - Proven experience leading, mentoring, and developing multidisciplinary technical teams. - Strong problem-solving abilities, strategic thinking, and a demonstrated passion for innovation and emerging technologies. - Experience working in regulated industries such as healthcare, financial services, or pharmaceuticals is advantageous. - Knowledge of AI governance, responsible AI, ethics, data privacy, and security considerations for enterprise AI solutions. - Excellent written and verbal communication skills in English; Spanish or Portuguese is a plus. ### Benefits - Base salary range of $210,000-$270,000, plus a 33% bonus opportunity. - Unlimited paid time off. - Healthcare, dental, and vision benefits. - Disability and life insurance. - Retirement benefits, including a 401(k) and potential employer match. - Paid parental leave, including up to 14 weeks for birthing parents and up to 4 weeks for non-birthing parents, subject to eligibility. - Paid adoption leave of up to 8 weeks, subject to eligibility. - Child hospitalization leave of up to 15 paid calendar days per year, subject to eligibility. - Approximately 12 paid company holidays annually. - Paid jury duty of up to 4 weeks per year, subject to eligibility. - Paid moving days of up to 2 days per year, subject to eligibility. - "Be kind to yourself day" on the work anniversary, subject to applicable terms. - Graduation and birthing gifts, subject to eligibility. - Remote work opportunity within the United States, with work-location arrangements subject to business needs. - Opportunity to work on cutting-edge AI initiatives while engaging with senior business and technology stakeholders. - Hands-on leadership exposure across AI architecture, consulting, innovation, and end-to-end solution delivery. ### Application and data-processing note Jobgether uses an AI-powered matching process to compare applications against the role's core requirements and shares a shortlist with the hiring partner. The partner manages final decisions and next steps. Jobgether states that applicant data is processed for candidate evaluation and sharing with the hiring employer under applicable privacy laws, including GDPR. AI tools may support resume review, response analysis, and inconsistency or verification signals, but Jobgether says final hiring decisions are made by humans.