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Genai Engineer Jobs in Washington (NOW HIRING)

Prompt engineering: Crafting and refining prompts to guide generative models to produce desired ... Experience integrating GenAI models and services into existing applications using APIs \n \n \n \n ...

Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients' success. You'll work alongside talented professionals ...

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 ...

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Genai Engineer information

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 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 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 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 popular job titles related to Genai Engineer jobs in Washington?

For Genai Engineer jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Genai Engineer jobs in Washington look for?

The top searched job categories for Genai Engineer jobs in Washington are:

What cities in Washington are hiring for Genai Engineer jobs?

Cities in Washington with the most Genai Engineer job openings:

Infographic showing various Genai Engineer job openings in Washington as of August 2026, with employment types broken down into 88% Full Time, 2% Part Time, 2% Temporary, and 8% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution.

$65 - $77/hr

Full-time

Posted 5 days ago


Job description

Job Title: GenAI Engineer
Location Preference: 100% onsite in Washington, D.C.
Duration: 1-Year Assignment with possibility of extension 
Contract Rate Range: $65-77/hr USD

NTT DATA is a team of more than 139,000 diverse professionals operating in more than 50 countries worldwide. Our sectors of activity include telecommunications, finance, industry, utilities, energy, public administration, and health.

Our mission? Offer technological solutions, business, strategy, development, and application maintenance while being a benchmark in consulting. Thanks to the collaboration between teams, the human quality of our people, and the fact that we do not conform to what is established, we always seek innovation that brings us closer to the future.

Our essence has led us to the forefront of technology, breaking paradigms and providing solutions that truly respond to each client's needs. Our talent has led us to be one of the top six technology companies in the world.

Because #Greattech, needs #GreatPeople, like you

NTT Data seeks high-achieving team players who quickly adapt to new challenges and entrepreneurial ventures. We are looking for a GenAI Engineer to work with our global client for a fully remote opportunity in LATAM working EST hours.

Position Summary

The GenAI Engineer is a core technical contributor responsible for designing, building, deploying, and managing AI and Machine Learning solutions across enterprise environments. This role focuses on implementing both classical ML and modern Generative AI workloads, including agent-based systems, Retrieval-Augmented Generation (RAG), and LLM-driven pipelines.
The engineer ensures all AI solutions are scalable, secure, governed, and aligned with enterprise architecture and operational requirements.

Key Responsibilities

  • Design, build, and deliver end-to-end AI/ML solutions-from experimentation and prototyping to production deployment.
  • Develop AI solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and related Azure AI services.
  • Build agent-based architectures using frameworks such as LangChain, LangGraph, Semantic Kernel, and MCP-style orchestration patterns.
  • Design and optimize prompt engineering strategies, RAG pipelines, embeddings, vector search, and knowledge-grounding workflows.
  • Build, train, evaluate, and deploy classical ML and GenAI models using Azure Machine Learning, including pipelines, feature engineering, model registry, and experiment tracking.
  • Implement MLOps and LLMOps practices including CI/CD, automated testing, responsible deployment, model monitoring, drift detection, and performance optimization.
  • Integrate AI solutions securely with enterprise systems, APIs, and event-driven architectures.
  • Embed Responsible AI principles-fairness, explainability, transparency, and human-in-the-loop controls-into solution design and development.
  • Collaborate closely with Data Engineers, AI Architects, Security teams, and business stakeholders to deliver scalable, compliant AI solutions.
  • Provide engineering guidance, mentor junior team members, and contribute to reusable components, shared libraries, and engineering best practices.

Requirements

Technical Skills & Platforms

  • Strong hands-on experience building and deploying AI solutions on Azure, including Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, and Cognitive Services.
  • Solid understanding of machine learning concepts including feature engineering, model training, evaluation, hyperparameter tuning, and operational deployment.
  • Experience deploying both predictive ML and GenAI solutions in enterprise settings.

Generative AI & Agent Systems

  • Hands-on experience with LLM-based system development, agent orchestration, and tool automation using frameworks such as:
    • LangChain
    • LangGraph
    • Semantic Kernel
    • MCP-style agent communication patterns
  • Experience implementing RAG pipelines, embeddings, vector databases, and document ingestion architectures.
  • Strong understanding of LLM constraints, prompt optimization, hallucination mitigation, and outputvalidation strategies.

MLOps, LLMOps & DevOps

  • Experience implementing CI/CD for ML and LLM workloads, including testing, monitoring, versioning, and automated deployment.
  • Familiarity with Azure DevOps pipelines, Git-based workflows, and cloud-native deployment automation.
  • Ability to balance rapid prototyping with strong engineering rigor, reliability practices, and production-readiness.

Cloud, Security & Governance

  • Understanding of cloud-native patterns, containerization, and scalable AI infrastructure.
  • Knowledge of identity, access management, secrets management, and secure deployment practices for AI systems.
  • Familiarity with Responsible AI frameworks and enterprise governance models.

Collaboration & Delivery

  • Ability to translate business problems into practical, scalable AI solutions.
  • Strong communication and cross-functional collaboration skills.
  • Experience working within Agile environments (Scrum, Kanban) delivering iteratively and incrementally.

Preferred Certifications & Training

  • Databricks Certified Generative AI Engineer Associate
  • Microsoft Azure AI Engineer Associate
  • Azure Machine Learning Certification
  • Azure Data Scientist Associate (optional)
  • MLOps or LLMOps training
  • LangChain/GenAI specialization coursework

Role Impact

This role is central to building and scaling enterprise-ready AI capabilities. It enables the development of secure, governed, highperforming AI systems that support organizational innovation, automation, and decision intelligence.

Why This Opportunity Is Attractive

  • Work with cutting-edge AI technologies and modern GenAI frameworks.
  • Lead hands-on development of AI systems deployed at enterprise scale.
  • Collaborate with cross-functional experts across architecture, engineering, and security.

Why NTT Data?   

Empowerment and rewards are the cornerstone of our career development model. We are a young, fast-growing company, with a highly innovative and entrepreneurial spirit, because of this professional experience and growth will be unmatched. Our talent and positive attitude allow us to transform our goals into achievements, and projects into realities.

NTT Data is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. NTT Data is an Equal Opportunity Employer Male/Female/Disabled/Veteran and a VEVRAA Federal Contractor.