Senior AI Platform Engineer

Modern Technology Solutions, Inc. (MTSI) is hiring a Senior AI Platform Engineer to work in Springfield, VA or St. Louis, MO. Qualified candidates must be U.S. citizens with an active TS/SCI clearance and be eligible to obtain a CI Poly.

We are seeking a hands-on Senior AI Platform Engineer to design, build, deploy, and operate production AI capabilities for NGA on a new Digital Ecosystem program. As a member of the Platform/Cloud team, this individual will develop the reusable services, integrations, agent harnesses, and operational practices that enable large language models (LLMs) and AI agents across the customer’s broader Digital Ecosystem.

The role combines architecture with direct implementation, including writing code, integrating models and tools, building deployment pipelines, evaluating AI performance, and troubleshooting production systems. Working alongside platform engineering, cybersecurity, enterprise architecture, and mission teams, this individual will turn emerging AI technologies into secure, reliable capabilities that support customer workflows.

The Senior AI Platform Engineer will also help develop an enterprise architecture digital twin, connecting UAF and SysML models with real-time and near-real-time operational data. These capabilities will enable AI-assisted analysis, data-driven insights, and visualizations of enterprise capabilities, dependencies, risks, and mission impacts.

Responsibilities:

• AI Platform Development: Design and implement reusable AI services, APIs, components, and reference architectures that support multiple applications and mission workflows across the Digital Ecosystem.
• LLM Integration and Engineering: Build LLM-enabled capabilities using model APIs, retrieval-augmented generation, prompt and context engineering, structured outputs, and inference services. Evaluate hosted, self-hosted, and open-weight models against security, performance, deployment, and cost requirements.
• Agent Harnesses and Workflows: Develop agent harnesses that manage model interactions, context, state, memory, tool access, workflow execution, and error handling. Implement bounded autonomy, human approval, execution limits, and recovery mechanisms for agents interacting with enterprise systems.
• Production Deployment and Operations: Build automated testing and deployment pipelines for AI services and agent workflows. Implement versioning, configuration management, monitoring, rollback, incident troubleshooting, and continuous improvement. Address reliability, latency, resource consumption, and operational cost.
• AI Evaluation and Observability: Develop representative evaluation datasets and automated tests for accuracy, groundedness, task completion, safety, and tool-use behavior. Instrument AI services to trace model interactions and agent actions, support troubleshooting, and detect performance degradation.
• AI Governance and Cybersecurity: Implement controls for approved models and tools, identity, least-privilege access, secrets management, sensitive data handling, audit logging, and third-party dependencies. Address prompt injection, data leakage, insecure tool execution, and AI supply chain risks in coordination with cybersecurity and governance teams.
• Enterprise Architecture Digital Twin: Work with enterprise architects and digital engineering teams to build a digital twin that connects UAF and SysML models with enterprise applications, infrastructure, services, and operational information. Support an evolving view of enterprise capabilities, dependencies, and conditions.
• Real-Time Model Integration: Build interfaces and event-driven integrations that associate real-time and near-real-time feeds with UAF and SysML model elements. Apply AI to correlate information, identify changes and inconsistencies, and propose validated model updates while preserving provenance, configuration control, and human oversight.
• AI-Enabled Insights and Visualizations: Combine architecture models and operational data to identify patterns, anomalies, dependencies, risks, and potential mission impacts. Develop AI-enabled dashboards, visualizations, natural language interfaces, and analytical services that make findings traceable to supporting information.
• Data and Knowledge Integration: Partner with data engineers and data owners to connect AI services with approved structured and unstructured sources. Implement retrieval, indexing, metadata, provenance, and access-aware information delivery within established data governance practices.
• Digital Ecosystem Integration: Connect AI services with Digital Engineering, MBSE, modeling, simulation and analysis, software development, and mission workflows through governed APIs and tool interfaces.
• Technical Collaboration: Contribute code, design reviews, documentation, demonstrations, and delivery estimates. Work with customer stakeholders and multidisciplinary teams to prioritize use cases, resolve technical issues, and transition prototypes into supported production capabilities.

Required Qualifications:

• Bachelor’s degree in computer science, engineering, mathematics, data science, or a related field, or equivalent relevant experience.
• 10+ years of combined relevant technical experience in software engineering, AI, data, cloud, or platform engineering, including at least 5 years developing, integrating, or applying AI or machine learning capabilities.
• Demonstrated hands-on experience building, deploying, and supporting production AI-enabled applications or services.
• Experience developing LLM-enabled applications using model APIs, retrieval-augmented generation, prompt and context engineering, or related techniques.
• Experience implementing agent-based workflows or AI application harnesses, including orchestration, tool integration, context management, and controls over agent actions.
• Strong proficiency with Python, APIs, version control, automated testing, and modern software engineering practices.
• Experience with containers, Kubernetes, CI/CD pipelines, and deployment automation.
• Experience implementing repeatable AI evaluations and monitoring application or model behavior in production.
• Understanding of AI cybersecurity and governance practices, including sensitive data protection, prompt injection defenses, least-privilege access, auditability, and human oversight.
• Experience integrating applications with enterprise systems, data sources, or operational feeds through secure APIs or event-driven interfaces.
• Ability to translate customer and mission needs into technical designs and working software.
• Ability to troubleshoot issues across AI applications, integrations, and supporting platform services.
• Strong written and verbal communication skills and the ability to collaborate with data, platform, cybersecurity, architecture, and mission teams.

Desired Qualifications:

• Experience operating self-hosted or open-weight LLMs, including inference optimization, GPU resource management, and capacity planning.
• Experience implementing LLMOps or MLOps practices, including model and prompt versioning, automated evaluation, deployment gates, monitoring, and rollback.
• Experience developing enterprise architecture digital twins or other model-driven decision-support capabilities.
• Familiarity with UAF, SysML v1 or v2, and integration of architecture models with external data, APIs, or analytical tools.
• Experience applying AI to correlate operational information with model elements, detect changes, or support controlled model maintenance.
• Experience building AI-enabled dashboards, visualizations, natural language query interfaces, or analytical applications.
• Experience with vector search, knowledge graphs, semantic models, or access-controlled enterprise retrieval.
• Experience with infrastructure as code, identity federation, secrets management, distributed tracing, and observability platforms.
• Experience conducting AI security testing, red teaming, or threat modeling for LLM applications and agent workflows.
• Experience supporting Intelligence Community, defense, or other regulated mission environments.
• Experience deploying AI capabilities in government cloud, restricted-network, disconnected, or edge environments.
• Familiarity with Digital Engineering, MBSE, Digital Twins, or modeling, simulation and analysis workflows.
• Experience supporting security accreditation, controlled data handling, and evidence-based compliance.
• Experience transitioning prototypes or research capabilities into sustained production operations.

Clearance Requirements:

• Must be a U.S. citizen with an active TS/SCI clearance and have the ability to pass a CI polygraph within 30 days.

The pay range for this position is $130,000 to $225,000/year; however, base pay offered may vary depending on established government contract ranges, job-related knowledge, skills, and experience, and other factors. MTSI also offers a full range of medical, financial, and other benefits, dependent on the position offered. Base pay information is based on market location. Applications will be accepted on an ongoing basis. This posting will be renewed periodically until the position is filled.