# Jobgether — Engineering Manager, AI Transformation

- Generated: 2026-09-15 11:30:43 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4466166972/
- Provider: LinkedIn
- Posted: approximately 2026-09-15 04:30 AM EDT (from '7 hours ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp when available
- Applicants: Be among the first 25 applicants
- Work model/location: Fully remote — United States; end employer is not identified
- Compensation: $185,200-$210,600 for Massachusetts; up to $235,700 in San Francisco
- Travel: Not disclosed
- Positioning track: Technical manager
- Fit outcome: PASS — 100%

## Direct-match strengths

Hands-on agentic AI, LLMs, RAG, MCP orchestration, evaluation frameworks, plugins and skills, Python, AWS, APIs, distributed systems, CI/CD, observability, regulated financial services, governance, security, team building, and measurable engineering/product outcomes.

## Hard or material gaps

The end employer is not identified. Keith's recent Senior Director, VP, and founder scope creates some down-level risk, but the hands-on player-coach mandate, disclosed Massachusetts compensation above $200,000, and exceptionally direct technical fit make the motivation credible. TypeScript is not claimed; Python directly satisfies the stated alternative.

## Evidence map

1. Lead, hire, coach, and develop an AI engineering team (weight 3, evidence 3/3) — Recruited and led 11-person teams, including a manager, with performance and career accountability.
2. Build and ship LLM-powered and agentic products (weight 3, evidence 3/3) — Built a production LLM/RAG/agent platform supporting 70+ automations.
3. MCP, evaluation, prompting, and orchestration (weight 3, evidence 3/3) — Direct current hands-on architecture and implementation.
4. AWS cloud-native architecture (weight 3, evidence 3/3) — Deep AWS platform, delivery, advisory, and operations experience.
5. Python, APIs, distributed systems, and CI/CD (weight 3, evidence 3/3) — Direct longstanding engineering evidence.
6. Security, compliance, auditability, and reliability (weight 3, evidence 3/3) — Direct governance, privacy, IAM, controls, 99.9% availability, and production operations.
7. Regulated financial-services environment (weight 2, evidence 3/3) — Direct MassMutual insurance, privacy, Responsible AI, and auditability work.
8. AI-assisted software development (weight 2, evidence 3/3) — Direct agentic coding, AI-enabled SDLC, automation, testing, and review practices.
9. KPIs for adoption, quality, velocity, and business impact (weight 2, evidence 3/3) — Direct outcome measurement across automation, reliability, cycle time, delivery, and adoption.

## Keyword diagnostic

Exceptional coverage across every core technical, management, governance, reliability, regulated-environment, and AI-transformation requirement; positioning stays player-coach and omits executive branding.

## Full normalized job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Engineering Manager, AI Transformation based in the United States.
This is a hands-on engineering leadership opportunity focused on accelerating responsible AI adoption across a fast-moving technology organization.
You will lead a team building and operating AI-powered solutions, including intelligent agents, integrations, evaluation frameworks, and usage analytics.
The role combines people leadership with deep technical ownership, giving you influence over architecture, engineering standards, and delivery strategy.
You will guide the development of agentic AI systems using modern LLM technologies, orchestration frameworks, and cloud-native architectures.
Working closely with Product, business teams, Security, Compliance, and AI governance stakeholders, you will translate emerging AI capabilities into measurable business value.
You will also establish the engineering practices needed to ensure solutions are secure, reliable, observable, scalable, and auditable.
This is an opportunity to shape an AI transformation function while growing a high-performing engineering team and advancing how AI is integrated into everyday software development.
Accountabilities
Lead, coach, and develop the AI Transformation engineering team, including hiring, performance management, career development, and team growth.
Build a high-performing and accountable engineering culture that encourages technical excellence, innovation, collaboration, and continuous learning.
Own the architecture, engineering delivery, quality, reliability, and operational health of AI solutions, including agents, plugins, skills, evaluation harnesses, integrations, and usage analytics.
Guide technical decisions involving agentic workflows, LLM integrations, MCP-based orchestration, and related AI development patterns.
Establish and enforce engineering standards covering security, compliance, auditability, reliability, and maintainability within a regulated financial services environment.
Champion modern software engineering practices, including AI-assisted development and agentic workflows that improve engineering productivity and delivery velocity.
Partner with Product, business units, Security, Compliance, and enterprise AI governance stakeholders to align technical execution with business priorities.
Lead delivery across initiatives involving multiple teams and engagement models, from consulting and co-development through ongoing ownership and maintenance.
Define and track KPIs related to AI adoption, solution quality, engineering velocity, reliability, and business impact.
Strengthen CI/CD, observability, production support, and operational processes across the AI solution portfolio.
Proactively manage technical debt, scalability, and platform maturity as AI adoption and the number of solutions increase.
Identify opportunities to expand the use of AI and agentic tooling throughout the software development lifecycle.
Requirements
7+ years of professional software engineering experience, including at least 2 years in technical leadership or engineering management.
Hands-on experience designing, building, and shipping LLM-powered or agentic AI products.
Strong practical knowledge of prompting, evaluation frameworks, agent frameworks, MCP, or comparable AI orchestration technologies.
Strong understanding of cloud-native architecture, preferably AWS or an equivalent cloud platform.
Experience with API design, distributed systems, modern CI/CD practices, and production-grade software engineering.
Proficiency in TypeScript and/or Python.
Demonstrated success hiring, coaching, mentoring, and developing engineers while managing evolving technical scope.
Strong stakeholder management and communication skills, with the ability to collaborate effectively across Product, business units, Security, Compliance, and technical teams.
Experience operating in regulated environments such as fintech, lending, payments, or other highly controlled industries is a plus.
Experience integrating AI or agentic development tools into the software development lifecycle to improve engineering productivity is advantageous.
Strong architectural judgment and the ability to balance innovation with security, reliability, compliance, and operational excellence.
Comfortable working in an ambiguous, rapidly evolving environment where priorities and technologies can change quickly.
Benefits
Competitive annual base salary depending on location:
$185,200–$210,600 in CA, WA, NJ, MA, DC, and NYC.
$207,900–$235,700 in the San Francisco market.
$169,100–$191,800 in other U.S. locations.
Potential eligibility for performance-based bonus compensation.
Potential eligibility for equity awards.
Fully remote work opportunity within the United States.
Opportunity to lead an engineering team at the forefront of enterprise AI transformation.
Hands-on exposure to LLMs, agentic AI, MCP orchestration, AI evaluation, and modern cloud-native technologies.
Significant influence over AI architecture, engineering standards, governance, and operational practices.
Opportunity to collaborate with cross-functional leaders across Product, business, Security, Compliance, and AI governance.
Career growth and team-building opportunities within a rapidly evolving AI-focused function.
How Jobgether Works
We use an
AI-powered matching process
to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice:
By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

## Artifact metadata

- Resume: https://bit.ly/46YcNL8
- Cover letter: https://bit.ly/4A95hdP
- Validation: PASS — 2-page resume (922 words), 1-page cover letter (211 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No