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Genai Jobs in Virginia (NOW HIRING)

GENAI DEVELOPER Location: Mclean,VA Duration: 12+ Months Visa: USC, GC, H1B and EAD Contract Type: W2 1) Agentic test automation foundation (reusable patterns + reference implementations) * Design ...

GenAI Engineer

Arlington, VA · On-site

$60K - $130K/yr

Role Overview We're looking for a hands-on, execution-oriented GenAI Engineer to help design, implement, and scale generative AI solutions across real client environments. This is not a purely ...

Role Overview We're looking for a hands-on, execution-oriented GenAI Engineer to help design, implement, and scale generative AI solutions across real client environments. This is not a purely ...

GenAI UAT Tester Location: Reston, VA Description: Seeking an experienced Data Analytics Engineer / Business UAT Tester with 7+ years of analytics, monitoring, visualization, production support, and ...

GenAI Developer

Reston, VA · On-site

$115K - $195K/yr

Recruiting for GenAI Developer experience to join our team in our Ashburn VA facility. This is a flexible onsite position. Must be able to report to the office as required. * Design, develop, and ...

GenAI Developer

Reston, VA · On-site

$115K - $195K/yr

Recruiting for GenAI Developer experience to join our team in our Ashburn VA facility. This is a flexible onsite position. Must be able to report to the office as required.Design, develop, and ...

Recruiting for GenAI Developer experience to join our team in our Ashburn VA facility. This is a flexible onsite position. Must be able to report to the office as required. * Design, develop, and ...

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Genai information

What are the typical day-to-day responsibilities of a GenAI specialist?

As a GenAI Specialist, your typical day may involve designing, developing, and optimizing generative AI models for various applications, such as natural language processing or image synthesis. You'll often collaborate closely with data scientists, software engineers, and product teams to align AI solutions with business needs. Tasks may include data preprocessing, model evaluation, fine-tuning algorithms, and addressing ethical or bias considerations in AI outputs. You may also participate in research initiatives, documentation, and client presentations, offering a dynamic and intellectually stimulating work environment.

What is a GenAI?

A GenAI (Generative AI) job involves working with artificial intelligence models that generate text, images, code, or other content. Roles in this field can include AI research, machine learning engineering, prompt engineering, or AI ethics. Professionals in GenAI work with large language models (LLMs) and deep learning frameworks to refine AI-generated outputs and integrate them into applications. These jobs are found in industries like tech, marketing, healthcare, and finance, where AI-powered automation and content generation are valuable.

What are the key skills and qualifications needed to thrive in the GenAI position?

To excel as a GenAI (Generative AI Specialist), you need a strong background in computer science, machine learning, and artificial intelligence, typically with a relevant degree and experience in training large language models. Familiarity with tools and frameworks such as Python, TensorFlow, PyTorch, and cloud platforms is highly valued, along with certifications like AWS Certified Machine Learning or Google Professional Data Engineer. Strong analytical thinking, creativity, and collaborative communication are standout soft skills in this field. These competencies ensure effective model development, seamless teamwork, and the ability to deliver innovative AI solutions in business contexts.

What job categories do people searching Genai jobs in Virginia look for? The top searched job categories for Genai jobs in Virginia are:
What cities in Virginia are hiring for Genai jobs? Cities in Virginia with the most Genai job openings:
Infographic showing various Genai job openings in Virginia as of August 2026, with employment types broken down into 86% Full Time, 7% Part Time, and 7% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Contractor

Posted 20 days ago


Job description

Job Title: GENAI DEVELOPER
Location: Mclean,VA
Duration: 12+ Months
Visa: USC, GC, H1B and EAD
Contract Type: W2

JOB DESCRIPTION
1) Agentic test automation foundation (reusable patterns + reference implementations)
  • Design and implement agentic testing patterns that can be adopted by multiple
  • Underwriting teams (and later other domains).
  • Create reference implementations (sample repos / templates) demonstrating:
  • Test generation assistance (from requirements, APIs, contracts, schemas)
  • Test maintenance assistance (auto-updating selectors/contracts, flaky test triage)
  • Failure analysis assistance (root cause suggestions, log correlation, defect drafting)
  • Establish a standard architecture for test code organization, tagging, data management,
  • and execution across UI + API + service layers.

2) Coverage standards, templates, and governance
  • Define and publish coverage standards (what "good" looks like) including:
  • Minimum coverage expectations by service/component
  • Test type mix (unit vs API vs UI vs contract vs integration)
  • Risk-based prioritization and traceability to requirements
  • Provide templates usable across teams:
  • Test plan templates
  • Test case/spec templates (Gherkin-style or equivalent)
  • Definition of Ready / Definition of Done quality checklists
  • Create a scalable tagging/metadata strategy (e.g., feature, service, risk, priority, data
  • sensitivity) to support reporting and quality gates.

3) GenAI-assisted reporting and quality insights across microservices
  • Build automated reporting that aggregates test + service data across multiple
  • microservices, such as:
  • Test execution results (Karate/Playwright + CI runs)
  • Service health signals (logs/metrics/traces if available)
  • Defect signals (issue tracker metadata if available)
  • Generate GenAI-driven summaries:
  • Release readiness narratives
  • Failure clustering and trend analysis
  • Produce outputs consumable by engineering leadership and teams (dashboards,
  • markdown summaries in PRs, artifacts in CI).

4) "Quality gates" via agents
  • Build automated review agents that evaluate user stories/requirements for minimum
  • required clarity and data before development/testing starts:
  • Required fields present (acceptance criteria, testable outcomes, data needs,
  • dependencies)
  • Ambiguity detection and missing edge cases
  • Data/privacy considerations and environment needs
  • Integrate gates into workflow (PR checks, issue templates, GitHub Actions) to reduce churn
  • and rework.

Required Technical Skills (must-have)
  • GenAI / LLM + agentic development
  • Hands-on experience building LLM-powered agents (tool-using, multi-step reasoning, guardrails).
  • Experience with prompting patterns, structured outputs (JSON schemas), evaluation, and reducing hallucinations.
  • Test generation/augmentation
  • Requirements review and completeness validation
  • Report generation and summarization
  • GitHub platform + GHCP (Copilot) for engineering workflows
  • Strong proficiency with GitHub Copilot in day-to-day development.

* Deep experience with GitHub platform capabilities:
  • GitHub Actions (CI/CD pipelines, reusable workflows, composite actions)
  • PR checks, branch protections, CODEOWNERS, templates
  • Automation via GitHub APIs/webhooks (as needed)
  • Test automation engineering (framework expertise)

* Advanced experience designing and implementing automation with:
  • Karate (API testing, contract-like checks, data-driven testing, mocks)
  • Playwright (UI automation, selectors strategy, parallelization, trace/video artifacts)

* Strong understanding of test design and coverage:
  • Happy path scenarios
  • Negative/validation scenarios
  • Edge/boundary scenarios
  • Data setup/teardown strategies and test isolation
  • Cross-service reporting and data aggregation
  • Proven ability to aggregate and normalize results from multiple microservices and multiple pipelines.
  • Experience producing actionable automated reports (trend analysis, failure clustering, service correlation).
  • Automated requirements review agents

* Experience implementing automated checks that validate:
  • Acceptance criteria completeness
  • Required test data and environment dependencies
  • Non-functional requirements (performance, security, observability) when applicable
  • Deliverables / What success looks like (for the posting)
  • A reusable agentic testing automation kit adopted by multiple teams.
  • Published coverage standards + templates and onboarding documentation.
  • A working GenAI-assisted reporting pipeline aggregating results across microservices.
  • Automated quality gates integrated into GitHub workflows that measurably reduce story churn.