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About This Role
Position Summary
The AI Platform Engineer implements AI evaluation, monitoring and secure software delivery capabilities supporting USPS OIG’s Responsible AI Framework. The role develops and tests technical controls, investigates model and data issues, and produces evidence that helps stakeholders assess AI reliability and risk. Responsibilities require hands-on AI/ML engineering, Databricks, Python, SQL, automation and CI/CD experience, with attention to maintainability and the needs of audit, review and investigative users. This position is contingent based on award, with an anticipated salary range of $175,000-$210,000 based on candidate qualifications and final contract requirements.
Work Environment
- Work Location: Arlington, VA and field offices; remote as Government-approved
- Security Clearance: Government suitability and access requirements to be confirmed
Compensation
Karthik Consulting is committed to providing competitive compensation based on the responsibilities of the role, required qualifications, security clearance level, relevant experience, certifications, customer requirements, geographic location, and overall business needs. The salary range listed for this position represents a good-faith estimate of the current compensation range for this role.
At Karthik Consulting, it is not typical for an individual to be hired at or near the top of the salary range. Final compensation decisions are based on the facts and circumstances of each candidate’s experience, qualifications, certifications, clearance level, contract requirements, and overall alignment with the role.
Employees may be eligible for company benefits and additional compensation programs, subject to company policy, contract requirements, and individual eligibility.
Program Context
Program Description
The USPS Office of Inspector General is strengthening its Responsible AI Framework to support the trusted use of artificial intelligence in audits, reviews, and investigations. The program builds on existing AI governance bodies and enterprise technology to advance ethical AI practices, workforce readiness, risk management, use-case evaluation, performance measurement, and data governance. Government personnel retain policy approval, risk acceptance, and decision-making authority.
Program Scope
The program encompasses enhancements to AI ethics controls, workforce guidance and training resources, AI risk registries and validation procedures, use-case intake and review workflows, performance monitoring and ROI measurement, and AI inventory and governance policies. Work includes configuration, integration, documentation, and reporting within the Government’s existing enterprise environment.
Key Responsibilities
AI Evaluation and Monitoring
- Develop and test AI evaluation routines using appropriate metrics, datasets and documented assumptions.
- Apply bias and fairness assessment techniques and interpret results in the context of intended system use.
- Implement model performance monitoring and investigate drift, data quality issues and unexpected behavior.
- Document evaluation limitations, technical findings and recommended corrective actions.
Software Engineering and Automation
- Develop maintainable Python and SQL code and integrate enterprise services through APIs.
- Implement automated testing, version control and repeatable delivery practices.
- Configure CI/CD checks and secure release controls within approved development environments.
- Troubleshoot code, pipeline and integration failures and verify the effectiveness of fixes.
Data Platforms and Observability
- Work with Databricks and related model management capabilities to support AI operations.
- Develop data processing routines and validate the accuracy and reliability of technical measurements.
- Create monitoring dashboards, logs and alerts that help users understand system performance.
- Apply approved identity, access and secrets management practices to engineering work.
Technical Quality and Collaboration
- Produce clear technical documentation, configuration records and operating procedures.
- Participate in design reviews and explain engineering options and tradeoffs.
- Resolve technical requirements and review test findings with data, security, governance and mission stakeholders.
- Maintain traceable testing evidence and transfer technical knowledge to designated system owners.
Expected Outcomes
Tested AI evaluation routines that produce interpretable bias, fairness and performance results with documented datasets, assumptions and limitations.
- Automated tests and release checks with reproducible results and evidence of failures, exceptions and corrective actions.
- Model monitoring, dashboards and alerts that identify performance changes and provide actionable information for investigation.
- Documented code, configurations and operating procedures that support troubleshooting, controlled changes and maintenance by designated owners.
Qualifications
Education
Bachelor’s degree in computer science, engineering, mathematics or data science, or equivalent relevant experience. A relevant master’s degree is preferred.
Professional Certifications
Preferred: Relevant certification in Databricks, cloud engineering, machine learning or DevOps.
Minimum Qualifications
- At least 7 years of relevant software, data platform or engineering experience, including demonstrated AI/ML evaluation and operational monitoring work.
- Hands-on Databricks implementation experience involving evaluation, model governance or monitoring.
- Experience implementing automated checks and release gates in GitHub, Azure DevOps or comparable CI/CD environments.
- Proficiency in Python, SQL, APIs, source control, automated testing and troubleshooting.
- Ability to implement bias/fairness checks, model metrics and drift analysis, documenting limits and data dependencies.
- Experience producing traceable technical evidence and collaborating with security and mission owners.
Preferred Qualifications
- Experience with MLflow, Unity Catalog, model registries and serving telemetry.
- Azure application services and Power BI or comparable dashboard integrations.
- Experience evaluating generative AI workflows and prompt behavior.
- Federal or regulated production delivery experience with controlled releases.
Job Category: AI/ML Job Type: Full Time Job Location: Arlington VA Education: Bachelor's Degree
Salary Context
This $175K-$210K range is above 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 Karthik 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 $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 ($192K) sits 9% below the category median. Disclosed range: $175K to $210K.
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.
Karthik Consulting AI Hiring
Karthik Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $210K - $210K.
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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