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Genai Engineer Jobs in Virginia (NOW HIRING)

PhoenixTeam

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 ...

PhoenixTeam

GenAI Engineer

Arlington, VA · Remote

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 ...

BTree Solutions, Inc.

GENAI DEVELOPER

Mclean, VA · On-site

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 ...

Sentrillion

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 ...

Sentrillion

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 ...

Sentrillion

GenAI Developer

Ashburn, VA · On-site

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 ...

Xenith Solutions LLC

GenAI Developer

Leesburg, VA · On-site +1

$115K - $165K/yr

Xenith Solutions is seeking a GenAI Developer with agile methodology experience to join our team in support of a Customs and Border Protection (CBP) contract. As a member of the team, you will ...

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Genai Engineer information

What are some typical challenges a GenAI engineer faces when deploying AI models in production environments?

GenAI Engineers often encounter challenges such as ensuring model scalability, addressing bias in generated outputs, and maintaining performance consistency in real-world applications. Deploying generative AI models requires careful monitoring to prevent unexpected or inappropriate outputs, as well as efficient resource management to handle large-scale computations. Collaborating closely with data engineers, product managers, and ML operations teams is essential to streamline deployment pipelines and quickly resolve issues that arise in live environments.

What is a GenAI engineer?

A GenAI Engineer is a professional who specializes in designing, developing, and deploying generative artificial intelligence (AI) models and applications. This role involves working with advanced machine learning techniques, such as large language models and generative adversarial networks, to create systems that can generate text, images, code, or other content. GenAI Engineers collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into products and services, ensuring ethical use and scalability. They also stay updated on the latest developments in AI research to continually improve model performance and effectiveness.

What is the difference between Genai Engineer vs Data Scientist?

AspectGenai EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with AI/ML frameworksDegree in Data Science, Statistics, or related fields; strong programming skills
Work EnvironmentDevelops AI models, fine-tunes generative AI systems, collaborates with AI teamsAnalyzes data, builds predictive models, interprets complex datasets
Employer & Industry UsageTech companies, AI startups, research labs focusing on generative AIFinance, healthcare, marketing, and tech firms analyzing data for insights

While both roles require strong technical skills and a background in data or AI, Genai Engineers focus on developing and deploying generative AI models, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap but serve different primary functions within AI and data-driven organizations.

What are the key skills and qualifications needed to thrive as a GenAI engineer, and why are they important?

To thrive as a GenAI Engineer, you need expertise in machine learning, deep learning, and programming languages such as Python, along with a solid understanding of generative models like GANs and transformers. Familiarity with frameworks such as TensorFlow or PyTorch, and experience with cloud platforms and MLOps tools, are highly valuable; advanced degrees or certifications in AI or data science are often preferred. Strong problem-solving, creativity, and communication skills help GenAI Engineers design innovative solutions and effectively collaborate with multidisciplinary teams. These skills ensure the development of robust, scalable generative AI systems that address complex real-world challenges.
What are popular job titles related to Genai Engineer jobs in Virginia? For Genai Engineer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Genai Engineer jobs in Virginia look for? The top searched job categories for Genai Engineer jobs in Virginia are:
What cities in Virginia are hiring for Genai Engineer jobs? Cities in Virginia with the most Genai Engineer job openings:
Genai Engineer jobs near you
Infographic showing various Genai Engineer job openings in Virginia as of August 2026, with employment types broken down into 92% Full Time, 2% Part Time, and 6% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.

GenAI Engineer

PhoenixTeam

Arlington, VA • On-site

$60K - $130K/yr

Full-time

Re-posted 10 days ago

Job description

PhoenixTeam is a remote-first organization and applicants for this position can work from anywhere within the US.
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 research role - and not a narrow "model tinkering" role either. You'll operate at the intersection of applied AI engineering, client delivery, and internal capability building, turning promising GenAI concepts into reliable, production-ready solutions.
You'll work closely with Value Engineers, clients, and internal stakeholders to match the right models and architectures to real business problems - especially in regulated, high-stakes environments. This role is ideal for someone who enjoys ownership, thrives in ambiguity, and wants to see their work move from prototype to production.
What You'll Do
  • Applied AI Engineering & Delivery
  • Design, implement, fine-tune, and deploy generative AI and ML solutions aligned to real business use cases.
  • Translate technical and business requirements into scalable AI architectures and implementation plans.
  • Lead or support end-to-end AI solution delivery, from experimentation through deployment and iteration.
  • Serve as the primary technical voice on GenAI initiatives, ensuring clarity across technical and non-technical stakeholders.
  • Model Selection, Tuning & Scaling
  • Evaluate and apply appropriate models (e.g., GPT-based LLMs, BERT-style architectures, or other approaches) based on problem context.
  • Fine-tune, integrate, and operationalize models with support from specialized teammates when needed.
  • Contribute to decisions around model performance, cost, latency, and reliability tradeoffs.
  • Support early MLOps practices, including deployment patterns, monitoring, and iteration.
  • Collaboration & Enablement
  • Partner closely with Value Engineers, product leaders, and clients to align AI solutions with business goals.
  • Communicate complex technical concepts clearly to non-technical audiences.
  • Contribute to internal knowledge sharing, patterns, and best practices for GenAI delivery.
  • Mentor teammates and support PhoenixTeam learning and growth initiatives.
  • Continuous Learning & Innovation
  • Stay current on emerging GenAI techniques, tools, and platforms.
  • Experiment with new approaches and bring practical insights back to the team.
  • Support PhoenixTeam's evolution in AI delivery methods, tooling, and ways of working.

What You Bring
  • Must-Have:
  • Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
  • Experience delivering technical projects that involve collaboration across stakeholders with varying technical depth.
  • Hands-on familiarity with generative AI concepts (e.g., LLMs, fine-tuning, embeddings) and a strong desire to deepen expertise.
  • Experience deploying or working with models on cloud platforms (AWS, Azure, or GCP).
  • Strong communication skills - you can explain why and how, not just what.
  • Ability to take ownership, manage priorities, and deliver outcomes in fast-moving environments.
  • Nice-to-Have:
  • Exposure to MLOps practices, containerization, or model monitoring.
  • Experience with AI/NLP applications beyond basic experimentation.
  • Familiarity with AI evaluation methods and performance metrics.
  • Interest in ethical AI, governance, and data security principles.
  • Experience in regulated industries (a plus, not a blocker).

What Success Looks Like:
  • Delivery Excellence: You produce polished, high-quality work and take ownership of outcomes - not just tasks.
  • Leadership & Resilience: You lead through influence, adapt quickly, and help teams stay aligned when things change.
  • Teamwork: You're a generous collaborator who adds value, shares knowledge, and steps in when help is needed.
  • Client Mindset: You care deeply about client outcomes and build trust through clarity, empathy, and follow-through.
  • Learning Champion: You're curious, self-directed, and committed to continuous growth.
  • Mentor & Coach: You support others through feedback, guidance, and shared problem-solving.
  • Operational Excellence & Innovation: You help improve how PhoenixTeam works - from internal processes to communities of practice.

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