AI-Native Python Full Stack Engineering

DXC Technology • New Orleans, LA

3d

About The Position

DXC Technology is seeking a hands-on engineering leader to transform Python full stack teams from traditional development practices to an AI-native engineering model. The role will define the strategy, tooling, and cultural changes needed to embed AI agents and copilots throughout the software development lifecycle, from requirements and design through coding, testing, and deployment. The position involves redesigning how a Python engineering organization plans, builds, and ships software, treating AI agents as collaborators alongside human engineers and establishing workflows, quality gates, and skills to support that approach.

The leader will define the vision and adoption roadmap for AI-native Python delivery; select and integrate AI coding assistants, agentic development environments, automated code review, and AI-assisted testing tools; and redesign development processes to incorporate AI at every phase. They will establish a center of excellence or working group, develop reusable resources and governance, and train engineers in AI-orchestrated development. The role also establishes quality, security, and governance guardrails, tracks outcomes such as velocity and code quality, and partners with internal and client stakeholders to pilot and scale effective practices.

The work location is flexible and may be remote or hybrid, with New Orleans, Louisiana, listed as a location. DXC describes its work model as prioritizing in-person collaboration while offering flexibility. The company states that it is an Equal Opportunity employer and participates in E-Verify.

Requirements

• At least 10 years of software engineering experience.
• Deep hands-on expertise in Python, including Django, FastAPI, or Flask, and full stack development.
• Experience with front-end frameworks such as React, Angular, or Vue; REST and microservices; and relational and NoSQL data stores.
• At least 3 years in an engineering leadership role, such as architect, engineering manager, or technical director.
• Experience leading teams of 10 or more engineers.
• Hands-on experience integrating AI coding assistants or agentic tools into real development workflows and production adoption.
• Strong understanding of modern CI/CD, DevSecOps practices, and cloud-native architecture on AWS, Azure, or GCP.
• Experience designing or redesigning engineering processes or playbooks at scale across multiple teams or a large program.
• Excellent communication skills and ability to translate technical AI-adoption strategy into business outcomes for engineering staff and executive stakeholders.

Nice To Haves

• Experience in a consulting or services environment delivering to external clients under contractual SLAs.
• Familiarity with LLM or agent orchestration frameworks, including LangChain, MCP, and agent-to-agent protocols, and with prompt engineering best practices.
• Experience establishing governance frameworks for AI-generated code, including security scanning, IP risk management, and model output validation.

Responsibilities

• Define the AI-native engineering vision for Python full stack delivery, including target-state workflows, tooling, and an adoption roadmap.
• Lead selection and integration of AI coding assistants, agentic development environments, automated code review, and AI-assisted testing and QA pipelines, ensuring interoperability with existing CI/CD, IDEs, and version control.
• Redesign software development lifecycle processes to incorporate AI agents into requirements decomposition, code generation, testing, code review, refactoring, documentation, and incident triage.
• Build and lead a center of excellence or working group for AI-native Python development, including reusable prompt libraries, agent configurations, AI-assisted coding standards, and governance for code quality and security.
• Develop training curricula and hands-on enablement programs to upskill engineers in prompt engineering, agent supervision, and reviewing AI-generated output.
• Establish quality, security, and governance guardrails for AI-generated code, including management of IP and licensing risk, secure coding practices, hallucination detection, and human-in-the-loop review gates.
• Define and track success metrics for developer velocity and throughput, code quality, defect rates, test coverage, cycle-time reduction, and cost-to-serve improvements attributable to AI-native practices.
• Partner with delivery leads, account teams, security and compliance teams, and client stakeholders to pilot AI-native delivery on live engagements and scale successful approaches.
• Monitor developments in AI coding agents, frameworks, and best practices, and continuously evolve the playbook.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed