# Signify Technology (confidential client) — Vice President of AI Engineering

- Generated: 2026-09-07 03:36:39 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4453703022/
- LinkedIn job ID: 4453703022
- Provider: LinkedIn
- Posting time: 2026-09-05 08:47:25 AM EDT
- Elapsed since posting at generation: approximately 1 day 18 hours 49 minutes
- Applicants: Not disclosed
- Work model/location: Florida hybrid or on-site preferred; remote considered nationally for the right leader. LinkedIn primary location is Orlando, Florida.
- Employment type: Full-time, permanent, executive level
- Compensation: $240,000–$310,000 base plus performance bonus, depending on experience
- Travel: Not disclosed
- Positioning track: Executive leader
- Fit outcome: FAIL — 89%

## Fit decision

FAIL under the optimizer's hard-gap rule. Keith directly matches the central executive mandate: AI strategy, budget ownership, organization building, hands-on AI engineering, production platform delivery, MLOps, governance, cloud infrastructure, and executive communication. However, the job explicitly requires strong large-language-model expertise including fine-tuning. Keith's source files document production LLM applications, RAG, agents, evaluation, model selection, open-weight models, TensorFlow, SageMaker, and MLOps, but they do not document hands-on LLM fine-tuning. The fit gate classifies an unsupported required technology as a hard gap, regardless of the otherwise high weighted coverage. No resume, cover letter, or LinkedIn connection note was generated without an explicit override.

## Weighted evidence map

| Requirement | Weight | Evidence score | Evidence |
|---|---:|---:|---|
| Own AI/ML strategy, roadmap, commercial alignment, and budget | 3 | 3 | Defined IDW AI strategy, roadmap, architecture, commercialization, engineering delivery, and a documented $900K budget; also owned strategy and budgets in prior leadership roles. |
| Build, lead, hire, mentor, and retain AI/ML/MLOps teams | 3 | 3 | Recruited and led 11 engineers at IDW, a seven-person team at SuccessKPI, a 24-person remote team at NorthBay, and global AWS technical communities totaling 180+ specialists. |
| Set architecture for LLM integration, RAG, agentic workflows, and fine-tuning | 3 | 2 | Direct production LLM, RAG, LangChain, LangGraph, agent, vector-search, and evaluation evidence; hands-on fine-tuning is not documented. |
| Move AI from prototype into reliable production with evaluation and monitoring | 3 | 3 | Delivered AssistX to production, 70+ AI automations across 20+ workflows, evaluation/guardrails/observability, three-nines uptime, and multiple other production platforms. |
| Own MLOps deployment, monitoring, drift, versioning, retraining, and lifecycle | 3 | 2 | Direct MLOps, SageMaker, deployment, evaluation, transparency, bias, drift, monitoring, auditability, and lifecycle-controls evidence; model retraining/versioning ownership is less explicit. |
| Own AI infrastructure, compute spend, model selection, inference economics, and build-versus-buy decisions | 3 | 2 | Direct AWS infrastructure, model evaluation/selection, LLM cost controls, cloud operations, budget ownership, and build decisions; quantified inference-economics ownership is not documented. |
| Establish responsible AI, governance, bias review, and data-handling practices | 2 | 3 | Led or advised five MassMutual privacy/governance initiatives and implemented evaluation, transparency, bias, drift, auditability, security, and human-oversight practices. |
| Partner across Product, Data, Security, vendors, and executives | 2 | 3 | Repeated cross-functional product, data, enterprise architecture, SRE, security, executive, customer, and vendor/model-landscape leadership. |
| Ten or more years in engineering and five or more years leading teams | 3 | 3 | More than 20 years of technical leadership and approximately 30 years of software architecture, including VP, Senior Director, Director, team-lead, and founder roles. |
| Deep Python/modern ML frameworks and cloud AI deployment | 3 | 3 | Direct Python, TensorFlow, SageMaker, AWS, CDK, Docker, Kubernetes, Bedrock AgentCore, data-platform, API, and production cloud deployment experience. |

Weighted result: 75 of 84 possible points = 89.3%, rounded to 89%.

## Direct-match strengths

1. Direct executive ownership of AI strategy, platform architecture, team building, budget, delivery, and operations.
2. Hands-on production GenAI credibility across LLMs, RAG, agents, evaluation, vector search, Python, APIs, and AWS.
3. Quantified delivery: an 11-engineer team, 70+ AI automations across more than 20 workflows, and selected work accelerated by up to 50×.
4. Enterprise-scale advisory and enablement through 200+ AWS customers and 180+ specialists.
5. Direct responsible-AI, MLOps, privacy, governance, monitoring, bias, drift, and auditability evidence.

## Hard and material gaps

1. **Hard gap:** hands-on LLM fine-tuning is an explicit job requirement but is not documented in Keith's source resume or career-context file.
2. Direct ownership of model retraining/versioning across a full production lifecycle is less explicit than his broader MLOps and governance record.
3. Inference-economics and compute-spend ownership are supported generally through budget, infrastructure, model selection, and LLM cost-control evidence, but quantified inference-cost outcomes are not documented.
4. The employer client is confidential until an initial conversation.
5. Remote work is conditional: Florida hybrid/on-site is preferred, while nationwide remote is considered for the right leader.
6. Applicant count and travel are not disclosed.

## Keyword diagnostic

- Evidence coverage: 18 of 21 core concepts (85.7%) have direct or adjacent source support.
- Strong coverage: AI strategy, engineering leadership, hiring, Python, TensorFlow, LLMs, RAG, agents, production AI, AWS, MLOps, evaluation, monitoring, drift, responsible AI, governance, platform budget, model selection, executive communication.
- Missing or materially weaker: hands-on LLM fine-tuning; explicit production model retraining/versioning ownership; quantified inference-economics outcomes.
- Diagnostic note: keyword coverage does not override an unsupported mandatory technology requirement.

## Full normalized job description

**Job title:** Vice President of AI Engineering

**Job type:** Permanent

**Salary:** $240,000 to $310,000 base plus performance bonus, depending on experience

**Role location:** Florida (hybrid or on-site) preferred. Remote considered nationally for the right leader.

### The company

Signify Technology is retained on a confidential search for a Vice President of AI Engineering. The client is past the pilot stage on AI and is hiring an executive to take it into production properly, with ownership of the strategy, platform, spend, and team. This is a genuine build seat rather than a caretaker one, reporting into the CTO or executive leadership, with a mandate to turn AI investment into shipped product. Because this is a confidential search, the client is named during an initial conversation. Senior leaders who would like to understand the opportunity before formally applying are welcome to reach out directly, and the conversation will be handled discreetly.

### Role and responsibilities

- Own the AI and machine-learning engineering strategy and roadmap, and align it to commercial outcomes.
- Build, lead, and grow the AI engineering, machine-learning, and MLOps teams, including hiring and mentoring.
- Set the architectural direction for AI systems, including large-language-model integration, retrieval-augmented generation, and agentic workflows.
- Take AI capabilities from prototype into production, with the evaluation, monitoring, and reliability that requires.
- Own the MLOps and model-lifecycle practice, covering deployment, versioning, drift detection, and retraining.
- Own AI infrastructure and compute spend, including model selection, inference cost, and build-versus-buy decisions.
- Establish responsible-AI practice, including model governance, evaluation standards, bias review, and data handling.
- Partner with Product, Data, and Security leadership to embed AI into the product and operating model.
- Evaluate the vendor and model landscape and own those commercial relationships.
- Act as the credible internal and external voice on what AI can and cannot deliver for the business.

### Job requirements

- Ten or more years in software or machine-learning engineering, with five or more years leading engineering teams at manager, director, or above.
- Proven ownership of an AI or machine-learning strategy and the budget behind it.
- Demonstrated track record of shipping AI or machine-learning systems into production at scale, not only proofs of concept.
- Deep, hands-on background in Python and modern machine-learning frameworks.
- Strong expertise with large language models, including fine-tuning, retrieval-augmented generation, and evaluation methodology.
- Solid MLOps grounding, including model deployment, monitoring, and lifecycle management.
- Experience deploying AI workloads in AWS, Azure, or GCP, with real awareness of inference economics.
- Track record of building, leading, and retaining an engineering organization.
- Legally authorized to work in the United States.

### Benefits

- Competitive base salary plus performance bonus.
- Medical, dental, and vision coverage.
- 401(k) with employer match.
- Paid time off and paid holidays.
- Executive ownership of the AI function, its platform, and its budget.
- A team the selected leader will build and lead directly.
- A real mandate and budget rather than an innovation-theatre seat.

Signify Technology states that it makes an active choice to be inclusive every day and asks applicants to request any accessibility adjustments needed during the application or interview process. Its diversity, equity, and inclusion statement says its mission is to empower every person, regardless of background or circumstances, with an equitable chance to achieve the career they deserve.

## Artifact metadata

- Archived JD capture: this file
- Resume: Not generated under the FAIL fit gate.
- Cover letter: Not generated under the FAIL fit gate.
- LinkedIn connection request: Not generated under the FAIL fit gate.
- Validation: Source identity, canonical URL, work model/location, posting status, compensation, fit evidence, and JD capture reviewed; Slack rendering must pass the fenced-block validator before delivery.
- Google Drive used: No