Quantum is a global technology partner delivering high-end software products that address real-world problems.
We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.
Here at Quantum, we are dedicated to creating state-of-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organizations globally attain technological leadership.
We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn’t yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!
About the position
Quantum is expanding the team and has a brilliant opportunity for a Senior Software Engineer — Infrastructure & Performance. The client is a pioneering technological research company that leverages proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in multiple languages. Its mission is to bridge the knowledge gap in the investment world and empower investors to become “super-investors.” Through generative AI technology integrated into brokerage platforms and financial institutions’ infrastructures, the company provides instant fundamental analyses of global stocks and enables informed investment decisions for millions of investors worldwide.
As a Senior Software Engineer — Infrastructure & Performance, you will own internal infrastructure, in-house tooling, and performance optimization across the stack. You will work on everything from Python services, Spark jobs, and data pipelines to internal AI tools built on LLM APIs, helping engineering teams work faster while making systems more efficient and cost-effective. This role requires strong technical ownership and autonomy: you will identify bottlenecks and developer experience pain points, determine what is worth improving, implement solutions, and measure their impact.
Must-have skills:
4+ years of experience shipping production Python
Proven experience finding and fixing real performance bottlenecks using profilers such as cProfile, py-spy, Scalene, memray, or flame graphs
Strong Computer Science fundamentals, including complexity, memory, caching, concurrency, and CPU- vs I/O-bound workloads
Hands-on experience building and tuning Apache Spark (PySpark) jobs in production, including partitioning, shuffles, joins, skew, caching, and Spark UI
Experience building production pipelines on LLM APIs such as Claude, Gemini, or OpenAI
Proven experience optimizing LLM cost and latency through model routing, prompt caching, batch APIs, context reduction, or response caching while measuring output quality
Production experience with Linux, Docker, and AWS or GCP
Experience with CI/CD and Infrastructure as Code using GitHub Actions, Terraform, Jenkins, or similar
Experience driving or actively contributing to an open-source, community, or inner-source project
Release engineering experience, including semantic versioning, changelogs, package publishing, and automated release pipelines
Experience with database performance optimization, including query plans, slow query diagnosis, and indexing
Strong ownership and ability to independently identify problems, propose solutions, and drive them through production
At least Upper-Intermediate level of English (spoken and written)
Nice-to-have skills
Hands-on experience with Databricks, AWS Glue, Amazon EMR, and Delta Lake
Experience with agent and AI tooling frameworks, including MCP, tool use, agent SDKs, RAG, and LLM evaluation tools such as Langfuse or LangSmith
Experience operating Kubernetes
Experience with distributed systems, queues, and streams such as Kafka, RabbitMQ, or Redis
Experience optimizing Python hot paths using NumPy/pandas vectoriza
