Machine Learning and AI Engineer

Quantum Sky•Remote,

1d•$135,000 - $155,000•Remote

About The Position

Quantum Sky is seeking a Machine Learning and AI Engineer to join Matrix, the digital platforms team within Project 137 in the Chief Technology Office. Project 137 develops applied research and proprietary technology to support mission needs across the company’s business units. In this role, you will develop and integrate AI/ML capabilities into digital platforms and enterprise applications. You will work across machine learning, generative AI, data engineering, and application integration, taking solutions from research and rapid prototyping through evaluation and production deployment. Working with Matrix engineers and stakeholders across Project 137, you will help deploy reliable AI capabilities on local hardware and in cloud environments, integrate them with enterprise systems, and communicate their value and limitations. The role is remote in the US, with hybrid-remote work in Reston, Virginia preferred. US citizenship and the ability to obtain a security clearance are required; a DoD/DoW Secret or Top Secret clearance is preferred. The typical salary range is $135,000–$155,000, with actual compensation determined by factors including skills, experience, education, and certifications.

Requirements

Demonstrated professional experience designing, developing, and supporting production software applications.
Strong hands-on Python software development experience, including professional application development with Django, FastAPI, or a comparable modern framework.
Programming experience with Go and familiarity with JavaScript/TypeScript or other modern application development languages.
Experience designing, developing, and consuming RESTful APIs and integrating applications with external systems and services.
Experience with relational databases, data modeling, backend application architecture, and modern front-end web development technologies.
Experience building AI agent or agentic-workflow systems involving tool use, multi-step orchestration, and integration with large language models.
Experience with modern front-end development, including at least exposure to generative or AI-driven UI.
Hands-on experience in cloud environments such as AWS, GCP, or Azure, and understanding of production application deployment, configuration, security, monitoring, and operations.
Experience in a CI/CD and DevOps-based software delivery environment, including automated builds, testing, deployments, version control, and release processes.
Familiarity with Infrastructure as Code such as Terraform or CloudFormation, sufficient to work effectively alongside the engineers who own it.
Experience with Docker, containers, and cloud-native application deployment.
Professional use of AI-assisted software development tools such as Claude Code, OpenAI Codex, Cursor, GitHub Copilot, or comparable technologies.
Ability to independently troubleshoot software, integration, deployment, and production issues and develop secure, reliable, and maintainable solutions.
Comfort operating in a high-ambiguity, early-stage product environment, including asking clarifying questions, proposing trade-offs, and iterating without fully specified requirements.
Strong analytical, problem-solving, and communication skills, with the ability to collaborate across software engineering, product, cloud, and platform teams.
US citizenship and ability to obtain a security clearance.

Nice To Haves

Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related technical field.
Direct experience with the Model Context Protocol (MCP) or comparable agent-tool integration standards.
Experience with agent orchestration frameworks such as LangChain, LlamaIndex, CrewAI, or comparable tools.
Experience developing software for network operations, telemetry, or mission/tactical systems.
Experience with AWS and/or GCP cloud-native services, including serverless, container, database, messaging, monitoring, identity, and security services.
Hands-on experience with Terraform, Docker, Kubernetes, GitHub Actions, GitLab CI, AWS CodePipeline, CloudFormation, or comparable DevOps and cloud automation technologies.
Experience with modern JavaScript/TypeScript frameworks such as React, including streaming UI patterns.
Familiarity with vector databases, retrieval-augmented generation, or other applied large-language-model patterns.
Experience with observability and monitoring platforms such as CloudWatch, OpenSearch, Splunk, Datadog, New Relic, or similar technologies.
Knowledge of the NIST Risk Management Framework (RMF), NIST SP 800-53 Revision 5, FedRAMP, and FISMA security and compliance requirements.
Experience translating operational or mission requirements into application features, automation, workflows, integrations, or reporting capabilities.
Prior experience supporting a Defense Information Systems Agency, Army, Air Force, or other Department of War program.
Experience developing and operating software supporting federal government, enterprise, or other highly regulated environments.
DoD/DoW Secret or Top Secret clearance.
Familiarity with Kubernetes or managed container platforms.

Responsibilities

Design, develop, train, evaluate, and deploy machine learning and deep learning models, including computer vision models such as YOLO, and export and serve models with ONNX.
Develop applications using commercial and open-weight large language models deployed on local hardware or in cloud environments.
Build and optimize retrieval-augmented generation (RAG) pipelines using document ingestion and parsing, embeddings, vector databases, and hybrid keyword and semantic retrieval.
Implement agentic AI workflows, including tool calling, orchestration, multi-agent coordination, and model context management.
Evaluate and improve models and AI applications through feature engineering, hyperparameter tuning, prompt design, fine-tuning, and performance optimization.
Implement AI guardrails for input validation, personally identifiable information, data classification, and safe failure behavior.
Integrate AI/ML capabilities into production applications and APIs using modular architectures, reusable components, containers, automated testing, and CI/CD pipelines.
Develop ETL/ELT pipelines for structured and unstructured data and maintain data quality, lineage, and appropriate handling of sensitive information.
Develop Power BI data models, DAX calculations, advanced analytics, and embedded reporting as needed.
Integrate solutions with Microsoft Azure services, including Azure OpenAI, and enterprise authentication using Microsoft Entra ID and OAuth.
Translate ambiguous requirements into technical approaches, research emerging methods, and prototype solutions to assess feasibility before production investment.
Contribute to architecture, technical planning, code reviews, and documentation, and communicate findings to technical and nontechnical audiences.