Roles and Responsibilities:
1.Design and develop computational finance applications with a focus on performance, scalability, and accuracy.
2.Implement high-performance and parallel computing solutions to optimize computationally intensive tasks.
3.Collaborate with actuarial teams to integrate financial models and simulations into scalable software systems.
4.Write clean, efficient, and maintainable code using best software engineering practices.
5.Conduct performance analysis, profiling, and optimizations to improve system throughput and latency.
6.Architect software systems that are modular, extensible, and maintainable
7.Design and implement APIs and libraries to support financial calculations and simulations.
8.Document technical decisions, development processes, and optimization results.
9.Keep up to date of emerging trends and technologies in high-performance computing, parallel processing.

Job Requirements:
1.Technical field Bachelor's / Master degree (e.g. Computer Engineering or Computer Science)
2.10+ years software engineer experience in developing / launching products, libraries and technologies within the actuarial / financial industry.
3.With Technical Lead experience, having led 5+ developers’ team and driven projects for whole life cycle.
4.Proficiency in programming languages like, Python, C++ or Java , with a focus on performance optimization.
5.Solid understanding of data structures, algorithms, and software design patterns.
6.Strong experience in parallel programming or distributed systems (e.g. CUDA).
7.Strong experience in GPU programming, vectorization, or other hardware acceleration techniques.
8.Familiarity with multithreading, concurrency, and asynchronous programming.
9.Experience in the Agile methodologies and software development life cycle (SDLC), including requirements gathering, design, implementation, testing, and deployment.
10.Experience in profiling, debugging, and optimizing code for performance.
11.Hands-on experience with cloud-based platforms (AWS, GCP, Azure) for scalable computing solutions.
12.Knowledge of version control tools (e.g. Git) and CI/CD workflows.
Preferred Experience
13.Experience developing software on Linux
14.Familiarity with QuantLib or similar quantitative finance libraries
15.Prior experience developing applications in computational finance, quantitative modelling, or risk analysis
16.Hands-on experience with numerical libraries (e.g., NumPy, SciPy) and tools for financial simulations
17.Knowledge of modern frameworks for distributed systems like, Dask or Spark