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About This Role
Job Description:
DXC Technology (NYSE: DXC) helps global companies run their mission-critical systems and operations while modernizing IT, optimizing data architectures, and ensuring security and scalability across public, private, and hybrid clouds. The world’s largest companies and public sector organizations trust DXC to deploy services across the Enterprise Technology Stack to drive new performance levels, competitiveness, and customer experience. Learn more about how we deliver excellence for our customers and colleagues at DXC.com.
- Clearance: Must be a US Citizen with an active Secret Security Clearance
- Work Environment: Hybrid, on-site 2-3 days, Crystal City, Arlington, VA
Responsibilities
- Design, develop, test, and maintain AI agents and generative AI applications using Python.
- Develop agent workflows that use large language models to interpret user requests, reason across multiple steps, select appropriate tools, retrieve information, and generate grounded responses.
- Design, test, and iteratively refine system prompts, task instructions, tool-use instructions, few-shot examples, response formats, and other prompt-engineering components.
- Apply prompt-engineering techniques to improve response accuracy, consistency, grounding, tool selection, adherence to business rules, and overall user experience.
- Develop agent orchestration logic including state management, context management, tool selection, multi-step execution, retries, exception handling, and recovery.
- Develop Python application components using boto3, botocore, and other appropriate SDKs and libraries to interact with AI, data, storage, security, and supporting cloud services.
- Integrate agents with approved tools, APIs, Model Context Protocol (MCP) services, databases, knowledge sources, and enterprise applications.
- Work with AI Integration Engineers to define tool requirements, expected inputs and outputs, validation rules, and integration behaviors needed by AI agents.
- Build and optimize retrieval-augmented generation (RAG) capabilities including query formulation, retrieval logic, context assembly, grounding, semantic search, and use of retrieved information within agent workflows.
- Develop prompt and context strategies for working with structured and unstructured enterprise information while minimizing irrelevant or unsupported model responses.
- Implement structured outputs, schema validation, response validation, guardrails, error handling, and other controls required for reliable enterprise AI behavior.
- Develop reusable Python libraries, utilities, agent components, prompts, and development patterns that can be used across multiple AI use cases.
- Create automated unit, integration, regression, and AI evaluation tests covering agent workflows, prompts, tool selection, model responses, retrieval quality, and application behavior.
- Develop and maintain evaluation methods for response quality, factual grounding, hallucination, tool-use accuracy, retrieval effectiveness, latency, consistency, and regression.
- Analyze model and agent behavior using logs, prompts, responses, tool calls, retrieved context, and downstream results to identify and correct performance or reliability issues.
- Compare and evaluate models, prompting approaches, retrieval strategies, and agent designs based on accuracy, reliability, performance, maintainability, security, and suitability for the use case.
- Collaborate with functional experts and Product Owners to translate business problems into clearly defined AI use cases, expected behaviors, acceptance criteria, and measurable outcomes.
- Support demonstrations, user testing, defect resolution, production validation, monitoring, and continuous improvement of deployed AI capabilities.
- Maintain source code, prompts, technical designs, configuration, evaluation criteria, support documentation, and other AI development artifacts.
- Participate in code reviews, architecture discussions, backlog refinement, demonstrations, testing, release readiness, and other Agile/SAFe delivery activities.
- Other duties as assigned.
Requirements
- Must possess an active Secret security clearance
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Systems, Engineering, or a related field; equivalent relevant experience may be considered.
- 3+ years of hands-on software development experience, including strong experience developing production-quality applications and services using Python.
- Strong Python development skills including object-oriented development, modules/packages, dependency management, exception handling, logging, testing, debugging, and API development.
- Hands-on experience developing AI agents, agentic workflows, LLM-based applications, or comparable generative AI capabilities.
- Strong hands-on prompt engineering experience, including development and refinement of system prompts, task instructions, few-shot examples, structured outputs, grounding strategies, tool-use instructions, and context-management approaches.
- Experience developing applications that interact with large language models through APIs or SDKs.
- Experience developing agent workflows that use tools, APIs, or MCP-based services, including tool selection, structured inputs and outputs, error handling, and integration of tool results into agent behavior.
- Hands-on experience using boto3 and botocore to develop applications that interact with cloud services and APIs.
- Experience with retrieval-augmented generation (RAG), semantic search, embeddings, vector search, or other knowledge-grounded AI techniques.
- Experience developing REST APIs, consuming APIs, working with JSON, and integrating Python applications with external services.
- Experience developing automated tests or evaluation methods for AI applications, including assessment of response quality, grounding, tool use, or regression behavior.
- Experience with Git/source control, code reviews, CI/CD, debugging, logging, and modern software-development practices.
- Strong troubleshooting skills and ability to diagnose problems across Python code, agent behavior, prompts, model responses, retrieval, tools, APIs, and downstream services.
Desired Skills
- Advanced experience developing agentic AI applications involving multiple tools, multi-step workflows, state management, or complex orchestration.
- Experience designing and optimizing production prompt libraries, agent instructions, reusable prompt templates, and prompt-evaluation approaches.
- Experience evaluating and mitigating hallucination, weak grounding, inconsistent responses, poor tool selection, excessive context, and other common LLM application problems.
- Experience with Amazon Bedrock, foundation-model APIs, or comparable managed generative AI services.
- Experience with agent-development frameworks such as LangGraph, LangChain, Strands Agents, or comparable orchestration technologies.
- Experience building RAG applications using embeddings, vector search, semantic retrieval, hybrid retrieval, document ingestion, or reranking.
- Experience with vector databases or Oracle AI Vector Search is a plus.
- Experience developing AI capabilities involving case management, knowledge management, help desk support, summarization, classification, recommendation, or workflow assistance.
- Experience consuming MCP services and integrating MCP tools into AI agent workflows.
- Understanding of AI application security considerations including prompt injection, inappropriate tool use, excessive agency, sensitive-data exposure, authentication, authorization, and least-privilege access.
- Working knowledge of SQL and relational databases, particularly Oracle.
- Experience integrating AI applications with PeopleSoft HCM, PeopleSoft CRM, or other complex enterprise applications is a plus.
- Experience developing containerized Python applications using Docker and deploying applications into cloud or Kubernetes environments.
- Familiarity with OCI or deployment of AI applications into Oracle Cloud Infrastructure is a plus.
- Experience working in Agile or SAFe environments and using Azure DevOps (ADO) or a similar lifecycle-management tool.
- Experience supporting secured Federal or DoD enterprise systems is preferred.
- Python, AWS, AI/ML, cloud, or related certification is a plus.
Additional Information
- Clearance: Must be a US Citizen with an active Secret Security Clearance
- Work Environment: Hybrid, on-site 2-3 days, Crystal City, Arlington, VA
Compensation at DXC is influenced by an array of factors, including but not limited to the experience, job-related knowledge, skills, competencies, as well as contract-specific affordability and organizational requirements. A reasonable estimate of the current compensation range for this position is $73,400 - $136,300. Full-time hires are eligible to participate in the DXC benefit program. DXC offers a comprehensive, flexible, and competitive benefits program which includes, but is not limited to, health, dental, and vision insurance coverage; employee wellness; life and disability insurance; a retirement savings plan, paid holidays, paid time off.
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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Salary Context
This $73K-$136K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($104K) sits 51% below the category median. Disclosed range: $73K to $136K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
DXC Technology AI Hiring
DXC Technology has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Newark, NJ, Arlington, VA, US, Mirando City, TX. Compensation range: $136K - $136K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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