About the company

An AI-native consulting firm rebuilding the Center of Excellence model for the AI era. It combines decades of traditional consulting experience with generative AI and automation to help organizations stand up self-optimizing, human-AI hybrid operating models, shifting clients from simply executing tasks to orchestrating outcomes across functions.

About the role

This engineer works at the point where a client's data, legacy systems, and AI-native solutions meet. It is not a back-office job. The person in this seat embeds with client teams and turns messy real-world data and aging systems into working AI-powered integrations, quickly.

The firm is looking for a builder-consultant. Candidates whose careers have been spent inside traditional systems integrators delivering templated, slow-moving projects will not find this a fit. The right person owns ambiguity, ships fast, and can adapt a solution live in front of a client.

Responsibilities

1. Deploy into client environments to build, integrate, and troubleshoot data and AI systems in real time
2. Design and build full-stack applications with a serious data backbone — from the interface all the way down to the data layer
3. Build and maintain data integrations, APIs, and connectors across a wide range of client systems: CRMs, ERPs, internal tools, and third-party platforms
4. Develop and deploy MCP (Model Context Protocol) servers and connectors that plug client data and tooling into AI workflows
5. Architect data pipelines and engineering workflows that make client data usable, reliable, and ready for AI
6. Prototype AI-powered features and agents on client data, then take them to production
7. Work directly with client stakeholders to scope, iterate, and debug — translating technical tradeoffs into language a business audience can act on
8. Own problems from end to end: from an ambiguous client ask, through architecture, to a shipped solution

What the company is looking for

1. Strong full-stack engineering skills paired with a real data background — not just calling APIs, but understanding data modeling, pipelines, and where quality breaks down
2. Hands-on experience building AI/LLM-powered applications
3. Time spent as a data engineer or working shoulder to shoulder with one: ETL/ELT, pipelines, warehousing
4. A track record building data integrations, APIs, and connectors across systems that were never designed to talk to each other
5. Familiarity with MCP or comparable tool and connector frameworks for AI systems
6. Genuine comfort in front of clients — presenting, troubleshooting live, adjusting scope on the fly
7. A builder's disposition: biased toward shipping, at ease with ambiguity, allergic to unnecessary process

Experience and qualifications

1. 2–3 years of professional experience in software or data engineering
2. Bachelor's degree in computer science, data science, engineering, or a related technical field — or equivalent practical experience

Compensation and benefits

1. Base salary of $140,000–$180,000 USD, commensurate with experience
2. Health benefits, PTO, and other standard benefits per the firm's U.S. employment policies

Travel

Travel within the United States is required as needed for client engagements, onsite deployments, and major project milestones. Frequency varies by client and project phase.

Work authorization

Candidates must be authorized to work in the United States. The firm does not currently sponsor employment visas for this role.

Equal opportunity

An equal opportunity employer. All qualified applicants are considered without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other legally protected characteristic.