# Jobgether — Engineering Manager, AI Transformation

- Generated: 2026-09-16 08:35:36 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4466828796/
- Provider: LinkedIn
- Posted: approximately 2026-09-16 03:35 AM EDT (from '5 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: United States; remote eligibility and end employer are not independently verified
- Compensation: $185,200-$210,600 in Massachusetts plus bonus and equity
- Travel: Not disclosed
- Positioning track: Technical manager
- Fit outcome: PASS — 97%

## Direct-match strengths

Hands-on agentic AI, LLM integrations, MCP orchestration, evaluations, AI-assisted engineering, Python, AWS, APIs, CI/CD, observability, security, auditability, regulated financial services, hiring, coaching, architecture, delivery, and operational ownership.

## Hard or material gaps

Jobgether does not identify the hiring employer, and the posting does not explicitly state the work model. PASS uses the skill's 80%+ verification exception; both unknowns remain material caveats.

## Evidence map

1. Lead, hire, coach, and develop an AI engineering team (weight 3, evidence 3/3) — Built and led multiple engineering organizations, including an 11-person AI team.
2. Production LLM and agentic products (weight 3, evidence 3/3) — Architected, coded, deployed, and operates production agents, RAG, tools, and workflows.
3. MCP, agent frameworks, prompting, and evaluation (weight 3, evidence 3/3) — Direct MCP, LangChain/LangGraph, prompt engineering, automated evaluation, and guardrail work.
4. AWS, APIs, and cloud-native architecture (weight 3, evidence 3/3) — Deep AWS and direct API, microservice, data, and cloud-platform implementation.
5. CI/CD, observability, reliability, and production support (weight 3, evidence 3/3) — Direct release, monitoring, incident, reliability, security, and operations ownership.
6. Security, compliance, auditability, and governance (weight 2, evidence 3/3) — Direct regulated financial-services privacy and AI-governance delivery.
7. AI-assisted engineering workflows (weight 2, evidence 3/3) — Mentored engineers in Codex and AI-assisted coding; over half of the platform used code generation.
8. Python or TypeScript (weight 2, evidence 3/3) — Eight years of direct Python experience.

## Keyword diagnostic

Near-direct match across agentic engineering, MCP, evaluation, AI-assisted development, Python, AWS, CI/CD, observability, regulated delivery, governance, people leadership, and production operations.

## 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 United States.
This role offers the opportunity to lead the engineering function behind an organization-wide AI transformation initiative. You will guide the development and operation of agentic AI solutions, integrations, evaluation frameworks, and AI-powered engineering tools. As a hands-on technical leader, you will shape architecture while building and scaling a high-performing engineering team. The position combines people leadership, deep technical ownership, and close collaboration with Product, business units, Security, and Compliance. You will help establish reliable, secure, and auditable AI practices within a regulated financial services environment. The role is ideal for an engineering leader who enjoys turning emerging AI capabilities into scalable solutions with measurable business impact.
Accountabilities
Lead, coach, and grow the AI Transformation engineering team, including hiring, performance management, career development, and building a high-performance and accountable engineering culture.
Own the architecture, delivery, reliability, and operational health of AI solutions, including AI agents, plugins and skills, evaluation harnesses, integrations, and usage analytics.
Guide technical decisions involving agentic workflows, LLM integrations, and MCP-based orchestration while establishing engineering standards for security, compliance, reliability, and auditability.
Champion modern engineering practices, including AI-assisted development and agentic workflows that can improve engineering productivity and delivery.
Partner with Product, business units, Security, Compliance, and Enterprise AI Governance teams to align engineering initiatives with business objectives.
Lead delivery across consult, co-development, and own-and-maintain engagement models, coordinating initiatives that span multiple teams.
Define and monitor KPIs related to AI adoption, solution quality, engineering velocity, and operational performance.
Drive CI/CD, observability, production support, and operational rigor across the AI solution portfolio.
Manage technical debt, scalability, and engineering maturity as the number and complexity of AI solutions continue to grow.
Requirements
7+ years of professional software engineering experience, including at least 2 years in technical leadership or engineering management roles.
Hands-on experience building and deploying LLM-based or agentic AI products, including prompting, evaluation frameworks, agent frameworks, MCP, or comparable orchestration technologies.
Strong understanding of cloud-native architecture, preferably AWS or an equivalent platform, along with API design and modern CI/CD practices.
Proficiency in TypeScript and/or Python.
Demonstrated experience hiring, coaching, and developing engineers while managing evolving team responsibilities and technical scope.
Strong stakeholder management and communication skills, with the ability to work effectively across Product, business units, Security, and Compliance.
Ability to make sound technical decisions while balancing architecture, delivery, reliability, security, and business requirements.
Experience working in a regulated environment such as fintech, lending, payments, or financial services is a plus.
Experience integrating AI or agentic development tools into the software development lifecycle to improve engineering throughput is a plus.
Benefits
Base salary range of $185,200–$210,600 for CA, WA, NJ, MA, DC, and NYC.
Base salary range of $169,100–$191,800 for other U.S. locations.
Base salary range of $207,900–$235,700 for San Francisco.
Eligibility for additional bonus compensation and equity.
Opportunity to lead an AI Transformation engineering function with broad organizational impact.
Hands-on exposure to LLMs, agentic AI, MCP-based orchestration, evaluation frameworks, and AI-assisted engineering workflows.
Leadership opportunity spanning people development, architecture, delivery, and operational excellence.
Cross-functional collaboration with Product, business teams, Security, Compliance, and enterprise AI governance stakeholders.
Opportunity to establish scalable AI engineering practices within a regulated financial services environment.
Reasonable accommodations are available for qualified applicants and employees with disabilities, as required by applicable law.
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/3T2OGrB
- Cover letter: https://bit.ly/4Ad3Mve
- Validation: PASS — 2-page resume (907 words), 1-page cover letter (212 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No