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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 Model Context Protocol (MCP) servers and supporting integration components using Python.
- Develop reusable MCP tools, resources, prompts, schemas, and interfaces that allow AI agents to interact with approved enterprise applications, databases, APIs, and data sources.
- Build MCP capabilities that securely expose enterprise functions such as search, retrieval, lookup, summarization support, case information, workflow information, knowledge content, and other approved business capabilities.
- Develop Python-based connectors and services that translate MCP tool requests into secure calls to enterprise APIs, databases, web services, and application interfaces.
- Design MCP tools with clearly defined inputs, outputs, validation rules, permissions, error handling, and operational boundaries.
- Implement secure read-only and transactional integration patterns, ensuring AI agents can perform only explicitly authorized actions.
- Develop and maintain REST/SOAP APIs, backend services, adapters, and supporting integration logic where MCP tools require access to existing enterprise systems.
- Integrate MCP services with Oracle databases, enterprise applications, case-management systems, knowledge repositories, and other approved data sources.
- Work with PeopleSoft technical teams to expose approved PeopleSoft HCM and CRM functionality through Integration Broker, Component Interfaces, web services, queries, APIs, database interfaces, or other appropriate integration mechanisms.
- Develop database queries and data-access components used by MCP tools while applying appropriate data filtering, validation, security, and access controls.
- Implement authentication and authorization for MCP servers and downstream enterprise connections using approved service identities, tokens, certificates, credentials, and cloud identity mechanisms.
- Apply least-privilege principles to MCP tools and service accounts so agents receive only the data and capabilities required for a specific use case.
- Implement input validation, output validation, schema enforcement, exception handling, retries, timeouts, logging, and other reliability patterns across MCP and API integrations.
- Prevent inappropriate direct access to enterprise applications or databases by creating controlled integration interfaces and reusable service boundaries.
- Develop mocks, simulated services, test harnesses, and automated integration tests to validate MCP behavior without requiring inappropriate use of sensitive production data.
- Troubleshoot issues across MCP servers, Python code, APIs, authentication, database connections, application interfaces, network connectivity, and downstream services.
- Analyze agent tool calls and integration behavior to identify incorrect tool usage, invalid parameters, excessive permissions, failed transactions, or other integration defects.
- Maintain MCP interface documentation, tool definitions, API specifications, data mappings, authentication requirements, deployment procedures, and operational support documentation.
- Establish reusable MCP development patterns, coding standards, naming conventions, security practices, and integration frameworks for use across multiple AI agents and use cases.
- Participate in code reviews, architecture discussions, backlog refinement, demonstrations, testing, release readiness, and other Agile/SAFe delivery activities.
- Other duties as assigned.
Requirements
- Must be a U.S. Citizen with an active Secret security clearance or be able to obtain one.
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, Engineering, or a related field; equivalent relevant experience may be considered.
- 3+ years of hands-on software development, systems integration, API development, middleware, backend development, or comparable enterprise integration experience.
- Strong hands-on Python development skills, including development of services, APIs, libraries, connectors, and integration components.
- Hands-on experience developing Model Context Protocol (MCP) servers, tools, resources, or comparable AI tool-integration capabilities.
- Experience designing and implementing integrations between applications, APIs, databases, and enterprise services.
- Strong knowledge of REST APIs, JSON, HTTP, authentication, authorization, input/output schemas, and enterprise integration patterns.
- Experience developing secure database integrations using SQL and appropriate database-access libraries or APIs.
- Experience implementing service authentication, secrets management, certificate or token-based authentication, least-privilege access, and secure service-to-service communication.
- Experience with integration reliability patterns including validation, retries, timeouts, exception handling, logging, monitoring, and failure recovery.
- Experience with automated testing, Git/source control, code reviews, CI/CD, debugging, and modern software-development practices.
- Strong troubleshooting skills across application code, MCP services, APIs, authentication, databases, and enterprise systems.
Desired Skills
- Advanced hands-on experience designing and developing enterprise MCP servers and reusable MCP tools for AI agents.
- Experience defining MCP tool schemas, resource interfaces, permissions, input validation, structured outputs, and secure execution boundaries.
- Experience developing MCP integrations with databases, case-management platforms, knowledge repositories, ERP systems, or other complex enterprise applications.
- Experience integrating AI agents with structured enterprise data and transactional business systems.
- Experience with Oracle Database and strong SQL skills.
- Experience with Oracle PL/SQL, including stored procedures, packages, functions, views, queries, and database-side business logic, is a strong plus.
- Experience integrating with PeopleSoft HCM or PeopleSoft CRM using Integration Broker, Component Interfaces, REST/SOAP services, PeopleTools, SQL, or related technologies.
- Experience developing or consuming REST/SOAP services using OpenAPI/Swagger or comparable API-definition standards.
- Experience with boto3, botocore, cloud SDKs, or related Python libraries used to access cloud-hosted services.
- Experience with OAuth/OIDC, service identities, certificates, API keys, tokens, and enterprise identity-management patterns.
- Understanding of AI-specific integration risks including prompt injection, inappropriate tool invocation, excessive permissions, data leakage, and unauthorized transactions.
- Experience implementing human approval, validation, or other controls around AI-initiated transactional actions.
- Experience with Docker, Kubernetes, OCI Functions, API Gateway, or comparable cloud-native application and integration technologies.
- Familiarity with OCI and deployment of MCP or AI integration services within secured Oracle Cloud Infrastructure environments 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.
- Oracle, Python, API/integration, 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: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 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 $213,380. 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/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 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).
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 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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