Cybermedia Technologies, LLC (CTEC)

Machine Learning Engineer

• Design and deploy autonomous AI agents capable of multi-step reasoning, tool-use, and self-correction to navigate complex federal benefit policies.
• Develop "Guardrail" layers that synchronize probabilistic LLM outputs with rigid business rules, ensuring benefit determinations adhere strictly to legal and regulatory frameworks.
• Implement advanced Retrieval-Augmented Generation (RAG) solutions, utilizing layout-aware parsing to extract information from dense manuals/documentation and unstructured data.
• Design and evaluate machine learning models that support both data-driven predictions and symbolic/rule-based automation.
• Deploy models into cloud environments with a focus on LLM-specific observability (tracing reasoning loops, monitoring for hallucinations, and detecting data drift in logic).
• Ensure every AI-driven determination has a clear, human-readable "audit trail" or reasoning chain that justifies the outcome based on source documentation.
• Work alongside solution architects and business stakeholders to translate complex health insurance policies into executable AI logic.

AI, Machine Learning Engineer

Full-stack Engineer

• Design and implement robust APIs and middleware that connect AI agents to a mixed-tech stack (Node.js, .NET, Python, etc.), ensuring seamless data flow and interoperability.
• Guide teams on best practices for integrating non-deterministic AI components into strictly typed mission-critical enterprise applications.
• Develop the orchestration logic that determines when a task is handled by a traditional rule-based system versus an autonomous AI agent.
• Build 'Validation Services' that sit between AI outputs and core systems to enforce rigid business rules.
• Use Python and PySpark to build robust data pipelines that feed high-context information into AI models while maintaining traditional SQL-based transactional integrity.
• Proven experience building and consuming RESTful APIs and managing service-to-service communication in a cloud environment (Azure preferred).
• Deep knowledge of Git, Azure DevOps, and containerization (Docker/Kubernetes).
• Establish deployment pipelines that include specialized testing for AI and interconnected components, such as prompt versioning, regression testing for model updates, and assisting with chaos-testing of logic flows.
• Lead and participate in Agile development ceremonies, manage work through DevOps tools, and maintain source control using Git-based workflows.

Senior Software Engineer

Business Analyst

• Business Process Analysis: Evaluate business processes, anticipate requirements, uncover areas for improvement, and develop actionable recommendations to enhance operations and system performance.
• Requirements Definition & Analysis: Lead requirements elicitation, documentation, and validation activities, translating business needs into functional and technical requirements for development teams.
• Agile & Scrum Participation: Participate in Daily Stand-ups, Development Team SCRUMs, Integrated Product Team (IPT) meetings, and Executive reporting sessions as required to support Agile delivery.
• Backlog & Workflow Management: Align processes, operating models, and delivery frameworks with Agile Scrum methodology and manage requirements, user stories, and backlogs using tools such as Azure DevOps to support iterative development and traceability.
• Stakeholder Engagement: Conduct meetings, workshops, and presentations to gather information, share findings, and align stakeholders on project objectives and solutions.
• Documentation & Communication: Document requirements, process flows, business rules, and analysis results; communicate findings and recommendations to cross-functional teams and leadership.
• Project Support & Oversight: Provide day-to-day project support including task tracking, progress reporting, and coordination across business and technical teams.
• Solution Validation: Ensure delivered solutions meet business needs and requirements through review, testing support, and stakeholder validation.
• Prioritization & Planning: Assist in prioritizing initiatives based on business needs, regulatory requirements, and program objectives.
• Stakeholder Liaison: Serve as a liaison between business users, government stakeholders, and technical teams to ensure alignment throughout the project lifecycle.
• Mentorship & Guidance: Provide guidance, training, and coaching to junior staff and support knowledge transfer activities.
• Works under moderate supervision with guidance from senior personnel.

• Design, develop, and enhance software applications and data-driven solutions using modern programming languages and frameworks to support enterprise business systems.
• Build and integrate software components and data services across cloud-based environments to ensure reliable system interoperability.
• Develop and maintain applications utilizing relational databases and data processing frameworks, with emphasis on SQL-driven data operations.
• Implement data processing scripts, automation routines, and distributed data processing logic using Python and PySpark to support data-intensive workflows.
• Write clean, maintainable, and well-documented code; participate in code reviews and maintain software documentation.
• Develop and execute unit and integration tests, debug defects, and resolve performance issues to improve system reliability.
• Participate in Agile development ceremonies, manage tasks through DevOps tools, and use source code control systems such as Git.
• Support development and deployment of applications within Azure-based environments.
• Identify opportunities to improve application performance, automation, and development processes.
• Work under moderate supervision with guidance from senior personnel.

Mid-level Software Developer

QA Engineer

• Develop, document, and execute manual and automated test cases to validate software functionality, data accuracy, and system behavior against requirements.
• Collaborate with business analysts and development teams to understand requirements and user stories, ensuring appropriate test coverage and traceability.
• Identify, document, track, and validate resolution of defects using lifecycle management tools such as Azure DevOps.
• Create and maintain automated test scripts and regression test suites to improve testing efficiency and repeatability.
• Perform Section 508 and accessibility compliance testing using tools such as ANDI, Color Contrast Checker, CommonLook, or similar accessibility evaluation tools.
• Assist in performance, integration, and system testing activities to ensure software meets functional and non-functional requirements.
• Generate test reports, document findings, and communicate testing status and risks to project teams and stakeholders.
• Participate in Agile/Scrum ceremonies including sprint planning, daily standups, sprint reviews, and retrospectives to support continuous delivery.
• Contribute to improving QA processes, test methodologies, and automation practices.
• Works under moderate supervision with guidance from senior personnel.

Mid-level Quality Assurance Engineer

Technical Project Manager

• Lead and manage Agile delivery teams using Scrum and Agile-at-Scale methodologies to deliver complex technology solutions on schedule and within scope.
• Create, manage, and maintain project roadmaps, sprint plans, and release schedules using Azure DevOps.
• Oversee requirements discovery and documentation in collaboration with business analysts and technical teams.
• Serve as the primary interface between government stakeholders, business users, and technical teams.
• Ensure technical solutions meet defined business, functional, and non-functional requirements.
• Identify and manage team blockers, conflicts, risks, and scope changes; maintain risk registers and issue logs.
• Facilitate sprint planning, daily standups, sprint reviews, retrospectives, and executive reporting sessions.
• Analyze current-state processes, define future-state improvements, and guide teams through change adoption and transformation.
• Coordinate across development, data, QA, architecture, and business teams.

• Lead the design and execution of comprehensive test strategies, test plans, and test frameworks to ensure software quality across enterprise systems and data-driven applications.
• Partner with business analysts, product owners, and developers to ensure requirements are testable, define acceptance criteria, and maintain end-to-end traceability using Azure DevOps.
• Lead manual and automated testing activities, ensuring adequate test coverage across functional, integration, system, regression, and user acceptance testing.
• Oversee defect lifecycle management using Azure DevOps, ensuring accurate documentation, prioritization, root-cause analysis, and resolution verification.
• Design, implement, and maintain scalable automated test frameworks and regression suites to improve testing efficiency and support continuous integration pipelines.
• Lead Section 508 and accessibility compliance testing using tools such as ANDI, Color Contrast Checker, CommonLook, and similar evaluation tools to ensure applications meet federal accessibility requirements.
• Plan and oversee performance, load, and integration testing to validate system scalability, stability, and reliability.
• Provide quality status reporting, risk assessments, and release readiness recommendations to project leadership and stakeholders.
• Serve as a quality advocate within Agile/Scrum teams, participating in sprint planning, daily standups, sprint reviews, and retrospectives.
• Mentor mid-level and junior QA staff, establish QA best practices, and support continuous improvement of testing processes and standards.
• Identify and implement improvements to QA methodologies, automation practices, and toolchains.
• Works under minimal supervision with minor guidance from senior personnel.

Senior Quality Assurance Engineer

Data Engineer

• Design, develop, and maintain scalable ETL pipelines and data workflows.
• Build, optimize, and maintain data processing solutions using Azure Databricks.
• Support phased data migration from legacy databases and ETL tools.
• Implement layered lakehouse data architectures in Databricks.
• Develop data processing notebooks, workflows, and distributed data transformations using Python and PySpark.
• Develop data validation, reconciliation, and testing processes.
• Integrate Databricks data platforms with analytics and reporting tools.
• Support data governance initiatives including metadata management.
• Maintain source control and CI/CD pipelines for Databricks workflows.
• Provide ongoing support for Databricks workflows and troubleshoot complex issues.
• Provide guidance to junior data engineers and contribute to documentation and team enablement.