About ALP

ALP Tech (Angel Lane Partners LLC-FZ) is a quantitative finance, data, and AI consulting firm. We build and deliver platforms across risk, regulatory, and portfolio-optimisation domains for GCC financial institutions — banks, asset managers, and corporate treasury — and for UK clients, with coverage across the GCC and UK plus an India Delivery Centre in Bengaluru.

The role

We are hiring a PhD-level engineer to build and own the numerical compute systems inside ALP's fixed income, derivatives, and portfolio-optimisation platforms. This is a deep technical role for someone who models financial and mathematical processes and writes their own code — covering algorithms, implementation, correctness, and performance across C++, Python, and Rust. Specifics of the product area (e.g.

XVA engines, portfolio optimisation, capital allocation) are shared during the interview process.

Responsibilities

- Build production systems — we build quant models and solvers that power client platforms, not research prototypes
- Implement numerical methods from research literature — fixed income, derivatives pricing, XVA, portfolio optimisation, capital allocation — into production code, verified against analytical solutions and reference data
- Design and own performance-critical compute systems in modern C++ and Rust
- Develop and optimise numerical solvers (e.g. convex and stochastic optimisation for portfolio construction), growing into performance-critical kernel-level work where needed
- Build correctness and performance regression testing into CI
- Write production-grade Python services (FastAPI/Flask) integrating quant models into client-facing platforms
- Engage directly with GCC banking clients on requirements and validation
- Raise the bar: testing discipline, code review, reproducibility

Requirements

- PhD (or equivalent demonstrated depth) in Mathematics, Physics,



Computational Science, Financial Engineering, or a related quantitative field
- You have built numerical models or solvers yourself — not configured commercial tools — and can walk us through the methods, the code, and how you verified correctness (convergence behaviour, analytical solutions, reference data)
- Robust programming ability across C++, Python, and Rust
- Deep numerical instincts: discretisation, stability, convergence, floating-point error, optimisation
- Comfortable working in the open-source/quantitative computing ecosystem — tools like QuantLib, Eigen, PETSc, IPOPT/CVXPY, NumPy/SciPy
- Evidence of code you own: thesis solver, open-source contributions, publications with repositories
- Willing and able to travel to the GCC approximately 30–50% of the time
- Able to join within a 1-month notice period
- Comfortable working in a startup/consulting environment with high ownership and agency

If you can pick up a quantitative finance or numerical methods paper, understand it, and implement it for a different problem from first principles — without vibe coding — we'd love to hear from you.

Nice to have

- Direct exposure to fixed income, derivatives (XVA), or portfolio optimisation
- Familiarity with GCC banking regulation — IFRS 9, Basel III/IV, RAROC, FTP
- Exposure to LLM/AI integration — vector databases, RAG, embeddings (e.g. pgvector, LanceDB)
- Prior experience delivering to GCC-region or financial-services clients

What we offer

- Deep technical ownership on a small, senior engineering team with Masters and PhD in UK, India and Dubai
- Direct client exposure across GCC banks and UK financial institutions
- Competitive, experience-based compensation

Process (~2 days)

1. Intro call — 15–30 min
2. Two rounds of interview
3. Final conversation → offer

Reach out to [email protected], with a relevant subject line and a short pitch about yourself.

📌 Quantitative Engineer — Scientific Computing (C++/Python/Rust) (Bengaluru)
🏢 ALP (Computational Finance, AI and Data Engineering)
📍 Bengaluru