Principal Engineer - Agentic AI Engineering

Agentic AI Engineering Principal Engineer Job Description This job is responsible for defining and leading the engineering approach for solutions at the program or portfolio level, to deliver significant business outcomes. Key responsibilities include continuously improving the design, quality, and reuse of the solution and delivering technology enablers that improve development efficiencies for the solution. Job expectations include familiarity with at least one area of engineering, acting as a “go to” reference across the organization, and applying knowledge to improve technical competencies through recruitment and development activities.

Developer Experience (DevEx) provides enterprise technical standards and common technical services, platforms, and tools that are leveraged by delivery teams across all lines of business. Within the SDLC Software Delivery Lifecycle program, this role leads portfolio product delivery strategy and execution for enterprise software delivery capabilities, ensuring the right investments, operating model, governance, and prioritization are in place to improve how internal technical users build, test, and deliver software at scale.

We are seeking a highly capable Agentic AI Engineering Principal Engineer to design, build, integrate, and support AI-enabled engineering capabilities embedded across the Software Delivery Lifecycle (SDLC). This role will help delivery teams adopt practical AI solutions that improve code authoring, test generation, automation, and developer productivity while aligning to enterprise engineering standards.

This role requires strong hands‑on engineering capability in one or more of GitHub Copilot, LangGraph, Semantic Kernel / Microsoft Agent Framework, SDLC automation, code and test generation, CI/CD integration, secure coding, and engineering productivity. The ideal candidate brings practical implementation experience, strong software engineering fundamentals, and a track record of turning AI-assisted development capabilities into reliable, secure, and scalable delivery workflows.

Responsibilities

Develops the engineering approach for the entire program/portfolio solution and works with Architecture, to develop/analyze/deliver the implementation of technical enablers

Leads the planning, definition, and design of the complex features which span multiple teams and explore solution alternatives

Creates ideas on designing complex technology and solution development approaches

Leads the technical oversight for teams in solution development including design reviews and code within own domain

Defines the technology tool stack for the solution within ranged of internally approved and supported technologies

Explores state-of-the-art technologies to improve development efficiencies, quality of test/QA coverage, and release management

Leads and is responsible for the end‑to‑end test strategy/creation/adherence, and the integration between teams for a program/portfolio solution

Improve the experience for our developers, making it easier to deliver industry‑leading solutions, while managing work efficiently and with the right controls

Advance our technology platforms through innovation

Reduce risk and improve quality across our technology portfolio by aligning to a single enterprise architecture strategy and delivering governance that enables consistency, integration and automation

Required Qualifications Engineering Leadership & Enterprise Platforms

7+ years of software engineering experience with hands‑on delivery across enterprise platforms, developer tooling, automation, or AI‑enabled engineering solutions

Demonstrated experience implementing shared engineering capabilities, reusable automation patterns, or platform integrations used across multiple teams

Experience engineering solutions in highly regulated environments with strong SDLC, risk, audit, and control requirements

Ability to work effectively with architects, platform teams, security partners, and delivery teams to translate standards into practical implementation patterns and working solutions

AI‑Assisted Engineering, SDLC Tooling & Automation

Hands‑on experience with

GitHub Copilot

and related AI‑assisted development workflows to improve code authoring, refactoring, documentation, and engineering efficiency

Practical knowledge of

LangGraph

and

Semantic Kernel / Microsoft Agent Framework

for building and integrating orchestrated AI workflows, tool connections, or engineering automation use cases

Experience implementing

SDLC automation

patterns that connect AI‑assisted capabilities to source control, build, test, release, and developer workflow systems

Strong understanding of practical engineering productivity improvements enabled by AI, including reduced manual effort, faster iteration, and improved delivery consistency

Code & Test Generation, CI/CD Integration & Delivery Workflows

Experience using AI‑assisted capabilities for

code and test generation , including unit tests, test scaffolding, refactoring support, and developer‑facing accelerators

Strong foundation in

CI/CD integration , with the ability to embed AI‑enabled workflows into build, validation, pull request, release, and quality control processes

Ability to implement delivery workflows that balance automation speed with traceability, control, and supportability in enterprise engineering environments

Secure Coding, Standards & Enterprise Enablement

Hands‑on collaboration with security, platform, and delivery teams to apply

secure coding

practices, review patterns, and controls to AI‑assisted engineering workflows

Strong understanding of enterprise engineering standards, practical guardrails, and implementation patterns that enable safe and consistent adoption of AI capabilities

Experience integrating AI‑assisted development capabilities with existing enterprise platforms and workflows in ways that are supportable, governed, and maintainable

Familiarity with rollout patterns, onboarding, documentation, and developer enablement approaches that improve adoption and responsible use of AI‑assisted tooling

Implementation Impact, Adoption & Engineering Productivity

Ability to implement reusable patterns, automation components, and developer enablement approaches that improve productivity, consistency, and speed to value

Proven track record delivering engineering capabilities from pilot to adoption through measurable improvements in workflow efficiency, code quality, automation, and developer experience

Demonstrated success connecting AI‑assisted engineering investments to reduced manual effort, improved test coverage, faster cycle times, and stronger delivery outcomes

Experience evaluating implementation options, tool fitness, and workflow design choices to guide teams toward practical, scalable, and supportable engineering use cases

Education

Bachelor’s degree in computer science, Engineering, Information Systems, Applied Mathematics, or a related technical field

Desired Qualifications

Advanced degree in a technical discipline or equivalent record of senior engineering experience in developer tooling, software delivery automation, AI‑assisted engineering, or enterprise platform integration

Skills

Automation

Influence

Result Orientation

Stakeholder Management

Technical Strategy Development

Application Development

Architecture

Business Acumen

Risk Management

Solution Design

Agile Practices

Analytical Thinking

Collaboration

Data Management

Solution Delivery Process

Shift 1st shift (United States of America)

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