Senior AI Platform Engineer

Modern Technology Solutions Inc•St. Louis, MO

6d•$130,000 - $225,000

About The Position

Modern Technology Solutions, Inc. (MTSI) is hiring a hands-on Senior AI Platform Engineer to work in Springfield, Virginia, or St. Louis, Missouri, on a new Digital Ecosystem program supporting NGA. The engineer will design, build, deploy, and operate production AI capabilities, including reusable services, integrations, agent harnesses, and operational practices that enable large language models and AI agents across the customer’s Digital Ecosystem. The role combines architecture with direct implementation, such as writing code, integrating models and tools, building deployment pipelines, evaluating AI performance, and troubleshooting production systems. The engineer will collaborate with platform engineering, cybersecurity, enterprise architecture, data, and mission teams to deliver secure and reliable capabilities for customer workflows. The role also supports development of an enterprise architecture digital twin that connects UAF and SysML models with real-time and near-real-time operational data. This work enables AI-assisted analysis, data-driven insights, and visualizations of enterprise capabilities, dependencies, risks, and mission impacts. The engineer will build integrations and analytical capabilities while preserving data provenance, configuration control, governance, and human oversight. Candidates must be U.S. citizens with an active TS/SCI clearance and be able to pass a CI polygraph within 30 days. The position is based in Springfield, VA, or St. Louis, MO. The listed pay range is $130,000 to $225,000 per year; base pay may vary based on contract ranges, job-related knowledge, skills, experience, and other factors. MTSI offers medical, financial, and other benefits dependent on the position offered. Applications are accepted on an ongoing basis, and the posting will be renewed periodically until the position is filled.

Requirements

Bachelor’s degree in computer science, engineering, mathematics, data science, or a related field, or equivalent relevant experience.
10 or more 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 ability to collaborate with data, platform, cybersecurity, architecture, and mission teams.
U.S. citizenship and an active TS/SCI clearance.
Ability to pass a CI polygraph within 30 days.

Nice To Haves

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.

Responsibilities

Design and implement reusable AI services, APIs, components, and reference architectures for applications and mission workflows across the Digital Ecosystem.
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.
Develop agent harnesses to manage model interactions, context, state, memory, tool access, workflow execution, and error handling, with bounded autonomy, human approval, execution limits, and recovery mechanisms.
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 for deployed AI capabilities.
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.
Implement AI governance and cybersecurity 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.
Work with enterprise architects and digital engineering teams to build a digital twin connecting UAF and SysML models with enterprise applications, infrastructure, services, and operational information.
Build interfaces and event-driven integrations associating 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.
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.
Partner with data engineers and data owners to connect AI services with approved structured and unstructured sources, implementing retrieval, indexing, metadata, provenance, and access-aware information delivery within established data governance practices.
Connect AI services with Digital Engineering, MBSE, modeling, simulation and analysis, software development, and mission workflows through governed APIs and tool interfaces.
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.