AI Platform Engineer

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.