Aerodynamics Methodology and Software Engineer

Description

Harmattan AI is building autonomous and scalable defense systems. As an Aerodynamics Methodology & Software Engineer, you will bridge the gap between aerospace physics and high-performance software engineering. Your mission is to professionalize our internal computational ecosystem, manage our HPC/Cloud infrastructure, and transform research-grade scripts into production-ready engineering software. You will act as the lead architect for our agentic AI workflows, developing custom MCP servers and LLM-ready repositories to automate complex simulation loops and design optimisations.

Responsibilities

• Software Professionalization: Refactor research scripts into modular, high-performance, and maintainable Python/C++ libraries. Implement robust unit-testing and documentation standards.
• AI-Augmented Engineering & Orchestration: Architect agentic workflows and custom MCP (Model Context Protocol) servers to bridge LLMs with internal CFD solvers and databases; codify engineering tribal knowledge into SKILLS.md or CLAUDE.md files to enable AI-driven code refactoring, automated simulation setup, and intelligent data analysis.
• MDAO Toolchain Integration: Architect a unified design environment by developing APIs and automated workflows that link disparate tools (e.g., OpenVSP, XFoil, and OpenFOAM) into seamless optimization loops.
• Infrastructure & Scalability: Manage and optimize our calculation infrastructure, including Linux-based HPC clusters (Slurm) and/or Cloud computing (AWS/Azure).
• Aero Database (ADB) Management: Design the data architecture for storing and retrieving high-dimensional aerodynamic results, ensuring the GNC and flight physics teams have "single-source-of-truth" access to vehicle performance data.

Qualifications

• Education: B.S., M.S., or Ph.D. in Aerospace Engineering, Computer Science, or Informatics with a proven track record in the aerospace sector.
• Aerospace Background: Familiarity with the aerospace physics, data structures and I/O of industry tools such as OpenFOAM, ANSYS, XFOIL, OpenVSP, or QProp.
• Advanced Programming: Expert proficiency in Python (NumPy, SciPy, Pandas) and ideally C++.
• Modern AI & Agentic Systems: Proven ability to develop LLM-integrated tools and MCP servers that automate engineering tasks; experience maintaining "AI-ready" repositories using structured instruction files and building neural surrogate models to accelerate physical simulations.
• DevOps & Infrastructure: Knowledge of Git/GitLab (CI/CD, Runners) and Linux/Unix environments. Experience with Bash scripting and Cloud-scale computing (AWS).

Nice-to-have

• Numerical Methods: Hands-on experience using CFD or other physical solvers. Able to select appropriate physics models, configuring numerical schemes, and fine-tuning solvers.
• Machine Learning in Data Science: Knowledge in setting up surrogate models and tools for engineering practices (surrogate models)