AI Engineer – Mission Innovation Lab

Carnegie Mellon University

AI Engineer developing mission-scale AI capabilities at SEI for national security. Translating AI concepts into robust solutions for the warfighting community.

Posted 5/21/2026full-timeArlington • Pennsylvania, Virginia • 🇺🇸 United StatesSeniorLeadWebsite

Tech Stack

Tools & technologies

AirflowDockerETLGraphQLJavaKubernetesPythonPyTorchRayReactTensorflow

About the role

Key responsibilities & impact

Design, develop, and fine-tune a variety of AI models
Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration; employ the Model Context protocol to manage stateful interactions
Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
Implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning; maintain model registries, monitoring, and version control of context protocols
Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI-safety techniques; translate new research into operational capabilities

Requirements

What you’ll need

Bachelor's degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related field
At least 8 years of relevant experience or a MS degree with 5 years of relevant experience
Proficiency in Python and at least one compiled language (C/C++ or Java)
Experience with REST/GraphQL APIs and containerization
Strong grasp of ML theory (supervised, unsupervised, reinforcement learning) and evaluation metrics
Hands-on experience fine-tuning LLMs and using frameworks such as Hugging Face Transformers, LangChain or comparable agent tools
Familiarity with building RAG pipelines (vector stores, dense/sparse retrievers)
Experience applying PEFT/LoRA methods to large models
Understanding of Model Context protocols for managing model state across multi-turn interactions
Experience building evaluation frameworks, benchmarks, or data quality pipelines
Experience with TensorFlow, PyTorch, or JAX; knowledge of data-pipeline tools (Airflow, Prefect, Ray) is a plus.
Awareness of DevSecOps practices (CI/CD, GitOps, container security scanning, model-registry concepts) is desirable.

Benefits

Comp & perks

Health insurance
401(k) retirement plans
Paid time off
Flexible work arrangements
Professional development opportunities

ATS Keywords

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Hard Skills & Tools

PythonC/C++JavaML theoryfine-tuning LLMsREST APIsGraphQL APIsETL processesRAG pipelinesevaluation frameworks

Soft Skills

mentoringcollaborationdesign reviewsdocumentation

Certifications

Bachelor's degreeMaster's degree