AI Engineer - Mission Innovation Lab

Overview

At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering questions related to the practical design and implementation of AI technologies and systems. We currently lead a community-wide movement to mature the discipline of AI Engineering for Defense and National Security.

As our government customers adopt AI and machine learning to provide leap-ahead mission capabilities, we

• build real-world, mission-scale AI capabilities through solving practical engineering problems

• discover and define the processes, practices, and tools to support operationalizing AI for robust, secure, scalable, and human-centered mission capabilities

• prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities

• identify and investigate emerging AI and AI-adjacent technologies that are rapidly transforming the technology landscape

Are you creative, curious, energetic, collaborative, technology-focused, and hard-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team.

• Design, develop, and fine‑tune a variety of AI models.

• Design autonomous agents and multi‑step pipelines using Lang Chain, 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 war fighting 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.

• Bachelor’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related field with at least eight (8) years of relevant experience, or a MS degree in the same with at least five (5) years of relevant experience.

• You will be subject to a background investigation and must be able to obtain and maintain an active Department of War (DoW) security clearance.

• You must be able and willing to work onsite 5 days per week at an SEI office in either Pittsburgh, PA or Arlington, VA.

• Proficiency in Python and at least one compiled language (C/C++ or Java); experience with REST/Graph
  
  QL 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, Lang Chain, or comparable agent tools.

• Familiarity with building RAG pipelines (vector stores, dense/sparse retrievers).

• Experience applying PEFT/LoRA methods (e.g., LoRA, adapters) 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 Tensor Flow, PyTorch, or JAX; knowledge of data‑pipeline tools (Airflow, Prefect, Ray) is a plus.

• Awareness of Dev Sec Ops  practices (CI/CD, Git Ops, container security scanning, model‑registry concepts) is desirable.

• Deploying LLM APIs (FastAPI, gRPC) at scale, handling latency and load balancing.

• Building multi‑tool agents, planner‑executor loops, or tool‑calling pipelines for complex decision‑making.

• Conducting adversarial testing, implementing input sanitization, and contributing to AI‑safety research.

• Utilizing GPU/TPU resources, mixed‑precision training, and distributed training frameworks such as…