MACHINE LEARNING ENGINEER

Air Space Intelligence is a technology company focused on decision-making across critical infrastructure domains such as aviation and defense. The role involves designing and deploying production-grade systems that integrate machine learning models into scalable software pipelines, while developing features that leverage ML for optimization and prediction problems.

Responsibilities:

• Design and deploy production-grade systems that integrate machine learning models into scalable software pipelines
• Develop and ship features that leverage ML to solve real-world optimization and prediction problems
• Work with modern infrastructure like Kubernetes, AWS, and MLOps tooling
• Approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale

Requirements:

• 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