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About This Role
Job Description:
About the Role
We are looking for a hands-on engineering leader to transform how our Java full stack teams build software, moving them from traditional development practices to an AI-native engineering model. This leader will work with our client to build the strategy, tooling, and cultural shift required to embed AI agents and copilots into every stage of the software development lifecycle, from requirements and design through coding, testing, and deployment.
This is not a "add Copilot licenses" role. It's a mandate to fundamentally redesign how a Java engineering organization plans, builds, and ships — treating AI agents as first-class collaborators alongside human engineers, with new workflows, new quality gates, and new skills to match.
Key Responsibilities
- Define the AI-native engineering vision for Java full stack delivery (Spring Boot/Spring Cloud, Angular/React, microservices, cloud-native architectures), including target-state workflows, tooling stack, and adoption roadmap.
- Lead tool selection and integration across AI coding assistants, agentic dev environments, automated code review, and AI-assisted testing/QA pipelines — ensuring interoperability with existing CI/CD, IDEs, and version control.
- Redesign SDLC processes to incorporate AI agents at each phase: requirements decomposition, code generation, unit/integration test generation, code review, refactoring, documentation, and incident triage.
- Build and lead a center of excellence (or working group) for AI-native Java development, including reusable prompt libraries, agent configurations, coding standards for AI-assisted output, and governance for AI-generated code quality and security.
- Upskill the engineering workforce, developing training curricula and hands-on enablement programs to shift developers from traditional coding to AI-orchestrated development (prompt engineering, agent supervision, code review of AI output).
- Establish quality, security, and governance guardrails for AI-generated code — including IP/licensing risk, secure coding practices, hallucination detection, and human-in-the-loop review gates.
- Define and track success metrics: developer velocity/throughput, code quality (defect rates, test coverage), cycle time reduction, and cost-to-serve improvements attributable to AI-native practices.
- Partner cross-functionally with delivery leads, account teams, security/compliance, and client stakeholders to pilot AI-native delivery on live engagements and scale what works.
- Stay current on the evolving landscape of AI coding agents, frameworks, and best practices, and continuously evolve the playbook. Required Qualifications
- 10+ years of software engineering experience, with deep hands-on expertise in Java, Spring Boot/Spring Framework, and full stack development (front-end frameworks such as Angular or React, REST/microservices, relational and NoSQL data stores).
- 3+ years in an engineering leadership role (architect, engineering manager, or technical director) leading teams of 10+ engineers.
- Demonstrated hands-on experience integrating AI coding assistants or agentic tools (e.g., GitHub Copilot, Cursor, Claude Code, Amazon Q Developer, or similar) into real development workflows — not just evaluation, but production adoption.
- Strong understanding of modern CI/CD, DevSecOps practices, and cloud-native architecture (AWS, Azure, or GCP).
- Experience designing or redesigning engineering processes/playbooks at scale (multiple teams or a large program).
- Excellent communication skills — able to translate technical AI-adoption strategy into business outcomes for both engineering staff and executive stakeholders. Preferred Qualifications
- Experience in a consulting/services environment, delivering to external clients under contractual SLAs.
- Familiarity with LLM/agent orchestration frameworks (LangChain, MCP, agent-to-agent protocols) and prompt engineering best practices.
- Experience establishing governance frameworks for AI-generated code (security scanning, IP risk, model output validation).
Location: Flexible (Remote/Hybrid — New Orleans,LA) At DXC Technology, we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing, productivity, individual work styles, and life circumstances. We’re committed to fostering an inclusive environment where everyone can thrive.
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Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At DXC Technology, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
DXC Technology AI Hiring
DXC Technology has 3 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span New Orleans, LA, US, Arlington, VA, US, Newark, NJ. Compensation range: $136K - $136K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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