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About This Role
Description:
Markon is seeking an AI/ML Engineer (Full Performance) to support an Intelligence Community client in McLean, Virginia. This position will design, develop, integrate, and maintain AI/ML-enabled applications supporting mission-critical enterprise capabilities, with a focus on large language model (LLM) integration, agent-based workflows, and retrieval-augmented generation (RAG). The AI/ML Engineer will work across application development, data engineering, and system integration to deliver scalable, reliable, and production-ready AI solutions that connect with enterprise tools, data sources, and existing systems. Responsibilities:
- Design, develop, implement, and maintain AI/ML-enabled applications, emphasizing LLM integration, agent-based workflows, and RAG capabilities.
- Develop and support agentic AI architectures, including orchestration logic, tool integration, memory management, context handling, and connections to external systems.
- Design, implement, and optimize RAG pipelines, including document processing, embedding generation, semantic retrieval, prompt assembly, and response optimization.
- Develop and maintain Python-based services, REST APIs, backend components, and user-facing application features supporting enterprise AI solutions.
- Integrate AI/ML applications with enterprise tools, databases, and structured or unstructured data sources.
- Process, organize, and transform data to support AI model integration, retrieval workflows, and application functionality.
- Develop and optimize PostgreSQL database schemas, queries, indexing strategies, and data workflows.
- Evaluate and benchmark AI/ML application and model performance using appropriate testing and evaluation methodologies.
- Optimize AI applications for reliability, latency, performance, usability, maintainability, and scalability.
- Apply software engineering best practices throughout the application development lifecycle, including testing, deployment, troubleshooting, and ongoing sustainment.
- Collaborate with technical stakeholders and development teams to translate mission requirements into functional, production-ready AI/ML capabilities.
Qualifications: Required:* Active TS/SCI clearance with Full Scope Polygraph.
+ Bachelor's degree and at least 3 years of related experience; OR + Master's degree and at least 1 year of related experience; OR + PhD with no additional experience required; OR + 6 years of equivalent relevant work experience and no degree.
- Experience developing AI/ML-enabled applications, including LLM integration, agent-based workflows, RAG, or similar retrieval-based capabilities.
- Proficiency in Python, common AI/data libraries, and AI frameworks supporting LLM application development.
- Experience implementing agent orchestration, tool integration, memory/context management, and connections to external systems.
- Strong understanding of AI/ML fundamentals, including transformers, machine learning algorithms, LLM behavior, data processing, and model integration.
- Experience developing or integrating REST APIs and Python-based backend services.
- Experience with PostgreSQL or other relational databases, including schema design, indexing, and query optimization.
- Understanding of production-ready software development practices, including application testing, reliability, deployment, performance optimization, and maintainability.
Desired:* Experience with Model Context Protocol (MCP) or similar protocols connecting AI agents to external tools, data sources, and enterprise systems.
- Experience developing full-stack AI or agent-based applications, including frontend interfaces, backend services, orchestration layers, and enterprise deployment.
- Experience with FastAPI, Flask, Docker, containerized development environments, and AWS or other cloud platforms.
- Experience optimizing LLM and agentic AI workflows, including prompt engineering, tool execution, memory management, context windows, latency reduction, observability, evaluation, and cost/performance tuning.
- Experience with MLflow or similar model evaluation and lifecycle tools.
- Experience implementing vector databases, embedding models, semantic search, and retrieval configuration.
- Experience processing and transforming structured and unstructured data, including PostgreSQL schema refinement and optimization.
Salary Range: USD $130,000.00 - USD $160,000.00 /Yr.
The Markon pay range for this position is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. Overview:
Eager to join a team where your skills are valued, your growth is nurtured, and your impact is profound? Look no further than Markon, a premier consulting firm deeply dedicated to advancing our nation's most critical missions.
At Markon, we don't just offer jobs – we offer opportunities for personal and professional transformation. Empowering our employees to lead, innovate, and excel, we foster an environment where new ideas are not just welcomed but celebrated. As a perennial Washington Post Top Workplace, we prioritize the well-being and success of our team members, ensuring they can bring their best selves to work.
Headquartered in Falls Church, Virginia, Markon has garnered national recognition for our unwavering dedication to excellence in serving the intelligence community, as well as federal civilian and defense agencies. Our growing reach extends across 17 states, 116 countries, and 5 continents, where our team of dynamic professionals collaborates to deliver unparalleled program and project management services.
Markon values people and the tremendous impact each individual can make – which is why we’re consistently recognized as one of the best places to work in federal government consulting. Here, you can help solve the nation’s most important challenges, surrounded by colleagues who help you grow, advance, and succeed. We are deeply dedicated to what matters – bringing out the best in each other to advance our clients’ missions.
Join us and make a meaningful impact.
Markon is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, national origin, age, protected veteran status, or disability status. This job posting will remain open until the position is filled. Benefits Offered: Medical, Dental, Vision, Life Insurance, Short-Term Disability, Long-Term Disability, 401(k) match, Flexible Spending Accounts, EAP, Training and Tuition Assistance, Paid Time Off, and Holidays
Salary Context
This $130K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Markon, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $130K to $160K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Markon AI Hiring
Markon has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span McLean, VA, US, Annapolis Junction, MD, US. Compensation range: $160K - $200K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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