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About This Role
Our Hiring Process
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1
### Apply Online
Submit your application through our careers portal
2
### Initial Review
Our team reviews your qualifications and experience
3
### Phone Screening
Initial phone or video conversation with our hiring team
4
### Technical Assessment
Role\-specific evaluation (if applicable)
5
### Team Interview
Meet with the team and discuss the role in detail
6
### Offer
Welcome to the Tiber Creek AI family!
### Senior AI Application Engineer New
Remote from VA/DC/MD Full\-Time Software Development $125,000 \- $165,000
Tiber Creek Ai is seeking a Senior AI Application Engineer to design and build secure, mission\-focused AI\-enabled applications using C\#, .NET, MCP, agent workflows, and modern cloud AI services. In this role, you will help turn practical AI use cases into production software by integrating large language models with enterprise systems, APIs, databases, documents, and human review workflows.
This is a hands\-on engineering role for someone who can move quickly from prototype to production while maintaining strong software engineering, security, testing, and documentation practices. You will collaborate with developers, product owners, security teams, and mission stakeholders to build useful AI capabilities that support real operational needs.
We offer generous medical, dental, life and disability insurance; flexible spending; 401(k) matching, ample vacation/leave time, as well as training/skill building opportunities and a great work environment.
Education:
BS/BA in Computer Science, Engineering, Information Systems, or related field. Equivalent experience may be considered.
Experience:
5\+ years of software engineering experience, including hands\-on experience building AI\-enabled applications, agent workflows, or LLM\-integrated systems.
Salary Range:
$125,000 \- $165,000
Clearance:
Must be clearable for a DoD Public Trust. Active clearance is NOT required to start.
Certifications:
Security\+ or an IAT Level II or III certification within 180 days of start is preferred.
Minimum Qualifications:
- Must be clearable for a DoD Public Trust.
- Residence in the DMV is preferred; occasional office/site visits for client engagements may be required.
- Must have experience building production software, not only proofs of concept or demos.
- Must be able to work with sensitive data, secure development practices, and enterprise access controls.
- Strong written and verbal communication skills are required.
- Unable to work with 3rd party candidates or agencies.
Related Experience:
- Strong C\# and .NET application development experience.
- Experience building ASP.NET Core APIs and integrating with enterprise systems.
- Familiarity with Model Context Protocol (MCP), agent workflows, tool calling, function calling, or workflow orchestration.
- Experience integrating LLM APIs such as Azure OpenAI, OpenAI, Anthropic, AWS Bedrock, or similar services.
- Experience with retrieval\-augmented generation (RAG), embeddings, vector search, and document processing.
- Understanding of prompt design, structured outputs, model evaluation, and AI safety considerations.
- Experience designing secure integrations with APIs, databases, document repositories, and internal systems.
- Solid understanding of authentication, authorization, access controls, logging, and auditability.
- Experience with SQL Server, PostgreSQL, or similar relational databases.
- Experience with Angular, ReactJS, or other modern front\-end frameworks is a plus.
- Experience working with source control, CI/CD pipelines, automated testing, and production monitoring.
Job Duties:
- Design and build AI\-enabled features using C\#, .NET, APIs, and modern cloud AI services.
- Develop and integrate MCP servers, tools, and agent\-based workflows.
- Build secure connections between LLMs and enterprise systems, databases, APIs, documents, and workflow tools.
- Implement retrieval\-augmented generation, structured tool calling, human\-in\-the\-loop review, and workflow automation patterns.
- Evaluate AI outputs for accuracy, reliability, safety, and mission usefulness.
- Establish reusable engineering patterns for prompts, tools, agents, testing, observability, and governance.
- Collaborate with product owners, developers, security teams, and stakeholders to translate use cases into working software.
- Participate in code reviews, technical design reviews, sprint planning, and delivery activities.
- Document AI system behavior, assumptions, limitations, risks, and operating procedures.
- Support troubleshooting, performance tuning, and production hardening of AI\-enabled applications.
Why Join Tiber Creek AI?
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### Mission\-Driven Work
Support critical federal missions and make a real impact on national security and government operations
### Cutting\-Edge Technology
Work with the latest AI, cloud, and low\-code/no\-code platforms to deliver innovative solutions
### Collaborative Culture
Join a supportive, family\-owned company that values teamwork, integrity, and personal growth
### Continuous Learning
Professional development, certification reimbursement, and educational opportunities
### Work\-Life Balance
Flexible work arrangements and generous PTO to support your personal well\-being
### Comprehensive Benefits
Medical, dental, vision, 401(k) matching, and more \- we invest in your future
Benefits \& Perks
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#### Generous Time Off
- 24 days PTO (first 5 years)
- 30 days PTO (5\-10 years)
- 36 days PTO (10\+ years)
- Flexible work arrangements
#### Comprehensive Health
- Medical coverage via Thatch ICHRA
- Monthly allowance aligned to age \& market
- Dental insurance through Sun Life
- FSA \& Dependent Care FSA
#### Financial Security
- 401(k) with 50% match up to 4%
- Performance bonuses
- Life \& disability insurance
- Referral bonus program
#### Career Development
- Education reimbursement
- Professional training
- Certification support
- Career advancement paths
### Equal Opportunity Employer
At Tiber Creek AI, we are committed to fostering a diverse and inclusive workplace where every voice matters. We provide equal employment opportunities to all employees and applicants, regardless of race, color, religion, gender, sexual orientation, national origin, age, disability, or veteran status.
Questions About Careers?
----------------------------
Our HR team is here to help answer any questions about opportunities at Tiber Creek AI
[email protected] (703\) 766\-2150
Salary Context
This $125K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tiber Creek Consulting, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($145K) sits 34% below the category median. Disclosed range: $125K to $165K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Tiber Creek Consulting AI Hiring
Tiber Creek Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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