Machine Learning Engineer (Defense)

Air space Intelligence, Inc.; Boston (MA); On-site; USD 150,000 - 200,000; Full time

Job summary

Air space Intelligence, Inc. is seeking a Defense ML Engineer to design and deploy production-grade systems that integrate ML models into scalable software pipelines. You will ship features using ML to solve optimization and prediction problems, leveraging modern infrastructure like Kubernetes, AWS, and MLOps tooling.

You will work with Python, ML frameworks, and LangChain in a collaborative, fast-paced environment, prioritizing robustness, maintainability, and performance at scale.

Qualifications

• Experience deploying production ML systems with robust data pipelines.
• Strong knowledge of Python and ML tooling/frameworks.
• Experience using LLMs in production, including prompt engineering and RAG.
• Solid understanding of data structures and algorithms.

Responsibilities

• Design and deploy production-grade systems integrating ML models into scalable pipelines.
• Develop features leveraging ML to optimize real-world problems.
• Work with Kubernetes, AWS, and MLOps tooling to build reliable systems.

Skills

Python

ML tooling

LLMs in prod

Data structures

Transformers

MLOps concepts

Testing pipelines

Tools

Kubernetes

AWS

MLflow

Apache Beam

LangChain

Job description

About ASI

ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible.

What You Will Do:

As part of our Defense engineering team, you will design and deploy production-grade systems that integrate machine learning models into scalable software pipelines. You’ll develop and ship features that leverage ML to solve real-world optimization and prediction problems, working with modern infrastructure like Kubernetes, AWS, and MLOps tooling. You’ll approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale.

What We Value:

• Proficiency in Python and experience with production ML tooling and frameworks (e.g., "TensorFlow", "PyTorch", "scikit-learn").
• Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain
• Strong understanding of data structures, algorithms, and software engineering best practices.
• Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.
• Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.
• Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.
• A bias for simplicity and clarity in solving complex problems.
• Intellectual curiosity and willingness to collaborate.
• Clear communication and collaboration across cross-functional teams.

How We Hire:

We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you.

ASI works with export controlled technology and restricted U.S. Government data, including on contracts mandating U.S. immigration status and location restrictions for performing personnel. Employment offers are contingent on ability to timely obtain all required authorizations for contemplated job duties.