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About This Role
Job Description:
About DXC Technology
DXC Technology is a leading enterprise technology and innovation partner delivering software, services, and solutions to global enterprises and public sector organizations — helping them harness AI to drive outcomes at a time of exponential change with speed. With deep expertise in Managed Infrastructure Services, Application Modernization, and Industry\-Specific Software Solutions, DXC modernizes, secures, and operates some of the world's most complex technology estates.
DXC's Insurance Software and BPS (ISB) helps insurers around the world modernize and run their core operations at scale by combining deep industry expertise, proven software platforms, and innovative AI\-driven solutions. A global market leader in core insurance platforms, ISB delivers solutions across policy administration, claims, billing, analytics, and digital engagement supporting Life \& Annuity, Property \& Casualty, and Specialty insurance markets. You’ll directly shape how the world's leading insurers operate by helping to transform the policy, underwriting, and claims systems that millions of people rely on every day.
AI Lead Engineer
Responsibilities:
- Understand the define technical vision, roadmap, and architecture for Generative AI and Agentic AI capabilities within the Assure Build Platform, ensuring scalability, security, and enterprise readiness.
- Lead the design and implementation of core GenAI platform components, including:
+ Agentic AI and multi‑agent orchestration
+ Retrieval‑Augmented Generation (RAG) pipelines
+ LLM model selection, configuration, and prompt tooling
+ Model fine‑tuning and customization pipelines
- Architect and govern AI agents capable of multi‑step reasoning, tool usage, memory management, and workflow orchestration, ensuring reliability, traceability, and controlled execution.
- Drive LLM inference optimization initiatives, including:
+ Prompt engineering and prompt tuning
+ Response and retrieval caching strategies
+ Latency reduction and throughput optimization
+ Cost governance across multiple model families
- Lead integration of Copilot Studio–based agents into Assure workflows, translating interaction history and usage patterns into reusable, production‑grade AI solutions.
- Partner closely with engineering, platform, product, and operations teams to embed GenAI capabilities into high‑impact Assure workflows such as migration acceleration, testing automation, performance analysis, and operational intelligence.
- Establish and enforce best practices for GenAI engineering, including secure prompt handling, evaluation frameworks, monitoring, logging, and responsible AI principles.
- Mentor and guide senior and junior engineers, cultivating deep technical expertise in GenAI, agentic systems, and platform‑first design.
- Promote a culture of innovation, experimentation, and learning, while maintaining strong governance, operational excellence, and long‑term platform sustainability.
Minimum Qualifications
- 8\+ years of professional software development experience building scalable, distributed, and maintainable systems.
- 3\+ years of experience in a technical leadership role, guiding teams through complex architectural and design decisions and setting high standards for performance, reliability, and code quality.
- Deep, hands‑on expertise in Large Language Models (LLMs), including:
+ Inferencing and runtime behavior
+ Embedding generation
+ Knowledge integration using Retrieval‑Augmented Generation (RAG)
- Strong, demonstrated experience with Agentic AI systems, including:
+ Design and implementation of single‑agent and multi‑agent architectures
+ Multi‑step reasoning and planning
+ Tool‑calling and workflow orchestration
+ Agent memory, state management, and traceability
+ Human‑in‑the‑loop and controlled execution patterns
- Proven experience designing and delivering enterprise‑grade GenAI platforms that support:
+ Agent orchestration and lifecycle management
+ Prompt engineering, versioning, and governance
+ RAG integration and evaluation
+ Model selection, configuration, and deployment
- Experience fine‑tuning, adapting, or customizing foundation models to improve task‑specific performance and domain alignment.
- Advanced knowledge of LLM and Agent inference optimization techniques, including prompt tuning, caching strategies, quantization, and latency reduction across different model families.
- Strong programming skills in Python (mandatory); experience with Java or equivalent languages for platform and systems engineering is a strong plus.
- Demonstrated ability to work cross‑functionally and influence product and platform direction through technical leadership and user‑centered thinking.
- Strong passion for operational excellence, observability, automation, and building secure, developer‑friendly AI and Agentic AI infrastructure at scale.
- Bachelor’s, Master’s, or PhD degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
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.
If you are an applicant from the United States, Guam, or Puerto Rico
DXC Technology Company (DXC) is an Equal Opportunity employer. All qualified candidates will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, pregnancy, veteran status, genetic information, citizenship status, or any other basis prohibited by law. View postings below .
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Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At DXC Technology, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
DXC Technology AI Hiring
DXC Technology has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Ashburn, VA, US, Nashville, TN, US. Compensation range: $189K - $189K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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