AI Software Engineer

Gallatin•Washington, DC

1d•$80,000 - $210,000•Onsite

About The Position

We’re looking for an AI/ML Software Engineer to play a foundational role in building and deploying AI-powered solutions for our core product. You’ll work across the full AI/ML lifecycle, from defining and building models to evaluating and deploying large-scale ML pipelines and real-time inference systems, while partnering with cross-functional teams to deliver impactful, real-world AI applications.

Requirements

Expertise in at least one: Python, C++, or C.
Hands-on experience with AI/ML training and fine-tuning frameworks, including: PyTorch, TensorFlow, CUDA, Jupyter Notebooks
Large Language Models (LLMs) and Open-Source Models (Llama, Anthropic, Mistral)
LangChain, Retrieval-Augmented Generation (RAG), Hugging Face, Exo Labs
Experience with data processing and pipeline frameworks such as Apache Kafka, Apache Airflow, AWS Kinesis, pandas, and dbt.
Understanding of AI model performance monitoring, data drift detection, and observability tools like Grafana or Kibana.
Deep understanding of deep neural networks (DNNs), LLMs, over/underfitting, prompt engineering, and LLM security (jailbreaking risks and protections).
A passion for continuous learning, staying current with the latest advancements in AI/ML, and teaching others

Nice To Haves

Experience mentoring and upskilling junior engineers.
Familiarity with legacy systems and working with public sector customers.
Contributions to open-source (AI/ML) projects.

Responsibilities

Build and Deploy AI/ML Models: Own and develop an AI model aligned with our core product needs within your first three months.
Validate AI-driven product features through real-world testing and feedback from warfighters.
Establish scalable MLOps pipelines and real-time inference services to streamline model training, deployment, runtime, and monitoring.
Own high model reliability and uptime by implementing monitoring across your work.
Data Collection & Processing: Identify and integrate structured, unstructured, batch, and real-time data sources.
Work with internal logs, APIs, user interactions, and third-party datasets to improve training data quality.
Continuous Improvement & Best Practices: Stay ahead of emerging AI trends and technologies to improve model performance.
Document and share AI/ML development best practices.
Contribute to the hiring and mentoring of AI/ML talent as we grow our team.
Help the wider team understand the latest trends and the significance of novel approaches in AI.