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Ai Implementation Jobs in Springfield, VA (NOW HIRING)

ECS

AI Methodologist

Fairfax, VA · On-site

... implementation strategy and guidance. The role involves designing AI solutions to enhance intelligence workflows and collaborating with analytic teams to address critical intelligence questions.

ECS

AI Methodologist

Fairfax, VA · On-site

$190K - $210K/yr

Develop and implement comprehensive implementation plans that enable analysts to leverage AI-enhanced workflows effectively. * Conduct on the job training to accelerate adoption of best practices.

AMA CONSULTING LLC.

AI Governance Lead

Lanham, MD · On-site

AI Governance Lead The AI Governance Lead will design and implement the AI governance framework embedded within AMA's AI assurance platform. This role will ensure alignment with federal AI policy ...

Cymertek Corporation

AI/ML Engineer

Chantilly, VA · On-site

Citizenship) Preferred : • Knowledge of reinforcement learning • Familiarity with generative AI models • Experience in edge AI implementation • Expertise in natural language processing • ...

New

Cymertek Corporation

AI/ML Engineer

Reston, VA · On-site

Citizenship) Preferred : • Knowledge of reinforcement learning • Familiarity with generative AI models • Experience in edge AI implementation • Expertise in natural language processing • ...

New

ECS

AI Methodologist with Security Clearance

Fairfax, VA · On-site

$190K - $210K/yr

Develop and implement comprehensive implementation plans that enable analysts to leverage AI-enhanced workflows effectively. * Conduct on the job training to accelerate adoption of best practices.

Vantor

AI/ML Engineer (Senior)

Reston, VA · On-site

$108K - $149K/yr

... AI implementation and represents capabilities to senior stakeholders • Active Top Secret clearance with CI Polygraph Preferred : • Experience with cloud platforms (e.g., AWS, Azure, or GCP) • ...

Citian

AI Integration Engineer

Washington, DC · On-site

$117K - $158K/yr

As an AI Integration Engineer at Citian, you'll lead efforts incorporating AI into our products, bridging civil engineering domain knowledge with practical AI implementation. You will work alongside ...

Cvent

AI Architect

Tysons, VA · On-site

$63.75 - $84/hr

Architect and implement the systems infrastructure underpinning client services platforms-designing for reliability, scalability, and security across all customer touchpoints. Own technical decisions ...

CELESTAR

Lead AI Architect - OBIQUA

Reston, VA · On-site

$57.50 - $78.75/hr

Required Qualifications: • An advanced degree in Computer Science, Data Science, or a related field. • Demonstrated experience leading AI implementation projects • Desired Experience: Minimum ...

Ignite IT

Jr. AI Prompt Engineer

Ashburn, VA · Remote

Support rapid prototyping, testing, implementation, and continuous improvement of AI solutions. * Document AI use cases, workflows, findings, lessons learned, and recommendations. * Collaborate with ...

Ignite IT

Jr. AI Prompt Engineer

Ashburn, VA · On-site +1

Support rapid prototyping, testing, implementation, and continuous improvement of AI solutions. * Document AI use cases, workflows, findings, lessons learned, and recommendations. * Collaborate with ...

Ignite IT

Jr. AI Prompt Engineer

Ashburn, VA

Support rapid prototyping, testing, implementation, and continuous improvement of AI solutions. * Document AI use cases, workflows, findings, lessons learned, and recommendations. * Collaborate with ...

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All JobsAi Implementation JobsAi Implementation Jobs in Springfield, VA

Ai Implementation information

See Springfield, VA salary details

$40.7K

$108.1K

$175.5K

How much do ai implementation jobs pay per year?

As of Jul 10, 2026, the average yearly pay for ai implementation in Springfield, VA is $108,128.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,900.00 and $126,400.00 per year, depending on experience, location, and employer.

How to get into AI implementation?

To pursue a career in AI implementation, develop strong skills in programming languages such as Python, understand machine learning frameworks like TensorFlow or PyTorch, and gain experience with data analysis and model deployment. Earning relevant certifications or degrees in computer science, data science, or AI can also enhance your qualifications.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI engineer, AI director, or chief AI officer, often found in large tech companies or organizations with significant AI initiatives. These roles usually require advanced skills in machine learning, deep learning, data analysis, and experience with AI tools and frameworks, along with leadership responsibilities. Compensation at this level reflects extensive expertise, strategic impact, and often includes bonuses or stock options.

What are the key skills and qualifications needed to thrive in the Ai Implementation position, and why are they important?

To excel in AI Implementation, you need a robust understanding of machine learning concepts, data analysis, and software development, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow, cloud platforms (AWS, Azure), and AI integration frameworks is commonly required, along with relevant certifications. Strong project management, problem-solving abilities, and excellent communication skills are crucial for coordinating with stakeholders and driving adoption. Mastering both technical and interpersonal skills ensures projects are delivered effectively and meet business objectives within diverse organizational settings.

How much do AI implementation consultants make?

AI implementation consultants typically earn between $70,000 and $130,000 annually, depending on experience, location, and industry. Senior consultants or those with specialized skills in machine learning and data analysis can earn higher salaries, often exceeding $150,000. Compensation may also include bonuses and benefits based on project success and company size.

What is an AI Implementation job?

An AI Implementation job involves deploying artificial intelligence solutions within an organization to improve efficiency, automation, and decision-making. Professionals in this role work closely with data scientists, engineers, and business teams to integrate AI models into existing systems. They manage data pipelines, ensure model performance, and address challenges related to scalability and compliance. Strong technical skills, project management, and an understanding of business processes are essential for success in this role.

What kinds of teams and departments does an AI Implementation professional typically collaborate with?

AI Implementation professionals usually work cross-functionally, interacting with data scientists, software engineers, IT departments, and business stakeholders to ensure AI solutions address specific business needs. Regular collaboration with product managers and operations teams helps align technical efforts with strategic objectives and regulatory requirements. You may also work closely with end users to gather feedback, refine implementations, and ensure a smooth adoption process. This collaborative environment not only enhances the quality of AI deployments but also offers valuable exposure to different aspects of the organization, fostering professional growth.

Which 5 jobs will survive AI?

AI implementation professionals, data scientists, cybersecurity specialists, healthcare providers, and skilled tradespeople are likely to continue thriving as these roles require complex problem-solving, human judgment, and hands-on skills that are difficult for AI to replicate. These jobs often involve critical thinking, emotional intelligence, or physical tasks that remain essential despite automation advances.
What job categories do people searching Ai Implementation jobs in Springfield, VA look for? The top searched job categories for Ai Implementation jobs in Springfield, VA are:
What cities near Springfield, VA are hiring for Ai Implementation jobs? Cities near Springfield, VA with the most Ai Implementation job openings:
Ai Implementation jobs near you
AI Implementation Engineer - JobID-0245

AI Implementation Engineer - JobID-0245

Innovative Defense Technologies (IDT)

Arlington, VA • On-site

$114K - $231K/yr

Other

Posted 10 hours ago

Posted today

Job description

About The Role:
Innovative Defense Technologies (IDT), a leading defense technology company, is seeking an AI Implementation Engineer to be part of our Warfare Systems team and based out of our Arlington, VA or Mount Laurel, NJ location.
The AI Implementation Engineer will design and deliver engineering-focused AI solutions that move beyond demos into reliable, mission-relevant systems. This role is ideal for an engineer who has extensive experience with commercially available AI tooling/chat, hosting LLM servers and has demonstrated ability to build end-to-end capabilities including MCP integrations, RAG pipelines, tool-using agents, and production-grade AI workflows.
Clearance & Location Requirements:
  • All applicants must be able to obtain/maintain an active Secret U.S. Security Clearance.
  • This is an on-site position. Requiring at least 3 days in office, based out of our Arlington, VA location or Mt. Laurel, NJ location.
Department Engineering Employment Type Full Time Location Arlington, VA Address 4401 Wilson Boulevard Suite 810, Arlington, Virginia, 22203 Open in Google Maps Workplace type Hybrid Compensation $114,000 - $231,000 / year Key Responsibilities
What You Will Do:
  • Design and Build AI Solutions for On-Prem systems in Air-Gapped environment: Design and implement end-to-end agentic AI systems that support planning, reasoning, tool use, and multi-step execution in real-world environments. Build modular, testable components that move from prototype to operational capability.
  • Integrate Models and Tools for On-Prem systems in Air-Gapped environment: Develop integrations across LLMs, APIs, data sources, and Model Context Protocol (MCP) interfaces to enable intelligent agents to interact with external systems, retrieve context, and take action safely and reliably.
  • Develop Retrieval Pipelines for On-Prem systems in Air-Gapped environment: Build and optimize Retrieval-Augmented Generation (RAG) pipelines that connect models to live knowledge sources, structured data, and enterprise content to improve factual grounding, contextual relevance, and response quality.
  • Engineer Conversational and Agentic Interfaces for On-Prem systems in Air-Gapped environment: Create conversational systems and intelligent agents with memory, contextual awareness, adaptive decision-making, and support for multi-turn user and system interactions.
  • Implement and Evaluate AI Workflows for On-Prem systems in Air-Gapped environment: Translate technical objectives into working pipelines, run experiments, evaluate agent behavior, and iterate on prompts, orchestration logic, retrieval quality, and system performance to improve reliability and usability.
  • Architect local infrastructure to size, config, and optimize local CPU/GPU workloads, utilizing quantization techniques to maximize throughput, etc.
  • Orchestrate disconnected environments, design and maintain offline model update pipelines, local package mirrors, etc.
  • Scope and Define Requirements: Gather, document, and validate technical and functional requirements from project artifacts, stakeholders, and mission needs to ensure feasibility, completeness, and alignment with operational goals.
  • Collaborate Across Teams: Work closely with engineers, technical leads, and mission stakeholders to integrate AI capabilities into broader software and system architectures. Participate in technical reviews, design discussions, and delivery planning.
  • Support Technical Quality: Contribute to testing, debugging, and performance optimization of AI-enabled applications, including edge cases involving context management, retrieval failures, tool execution, and orchestration logic.
  • Learn and Apply Emerging Practices: Stay current on advances in LLMs, agent frameworks, orchestration methods, and applied AI engineering practices, and bring that knowledge into practical system design and implementation.
  • Communicate Technical Work: Clearly document architectures, workflows, assumptions, and implementation decisions so that solutions are maintainable, explainable, and transferable across teams.

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