Mid-Level AI Engineer (NOT SENIOR)

Job Title:

Mid-Level AI Engineer (NOT SENIOR) Location:

Washington, DC -

Onsite 5 days per week

(local candidates only - must live in Washington, DC area) Work Arrangement:

Onsite Interview Mode:

Video +

In-Person Interview Experience Required:

4-7 years

Position Overview

The AI Engineer will be part of a Tech Consulting firm's Forward Deployed Engineering team supporting the Client's AI-Assisted SDLC Pilot Program. Working closely with the onsite AI Architect, this role is embedded directly alongside the Bank's development squads - configuring AI tooling, operating governance controls, and enabling developers through daily paired coding sessions. The AI Engineer reports to the Tech Consulting Company Engagement Lead and plays a critical role in ensuring the pilot produces measurable, evidence-based productivity outcomes.

Must-Have Skills 5+ years of software engineering experience

across all technologies and back-end technologies Hands-on experience with AI coding assistants (e.g., GitHub Copilot, Claude Code, Cursor, Amazon Q Developer)

in day-to-day development workflows Strong proficiency in modern web frameworks, RESTful APIs, and at least one major cloud platform (AWS, Azure, or GCP) Solid understanding of CI/CD pipelines, Git workflows, pull request reviews, and automated testing practices Experience with static analysis tools (SAST), code quality gates, and security scanning

in enterprise development environments Ability to write clear technical documentation, training materials, and process guides Bachelor's degree in Computer Science, Software Engineering, or a related field Must speak clear American English Key Responsibilities

Participate in codebase assessments to evaluate the Bank's existing technology stack, development workflows, and AI readiness (multi-programming languages - Python and Java to be able to look at code base) Configure and maintain squad development environments, including AI coding tools, context files, and agent configurations within the Bank's infrastructure Set up and operate governance tooling including PromptBOM logging, SAST rulesets for AI-generated code, and secrets scanning pipelines (Python) Embed daily within Bank engineering squads during pilot execution - reviewing AI-flagged pull requests, pair-programming, and coaching developers on AI-assisted workflows Build and refine custom agents, prompt libraries, and steering files based on pilot learnings and squad feedback Document ways of working, training materials, and the AI-SDLC playbook content in collaboration with the AI Architect Transition from embedded driver to coaching role during the Optimization & Expansion phase, guiding new squads through onboarding Harden the agent library and toolchain configurations for production-readiness during capability transfer Support the train-the-trainer program by teaching Bank engineers to independently configure, maintain, and extend the AI tooling Additional Skills (Plus)

Experience with prompt engineering or building custom AI agents/skills Familiarity with developer productivity measurement frameworks (e.g., SPACE, DORA metrics) Prior experience working on client-embedded or forward-deployed engineering engagements Knowledge of compliance and governance requirements in regulated industries (financial services, government) Experience mentoring or training junior developers or conducting technical workshops Candidate Preferences

Prefer candidates with investment banking, investment management, asset management experience (retail/commercial bank experience is also acceptable)