Quant Data & AI Platform Engineer
CapsuleHR
About the Role
CapsuleHR is partnering with Algocor to find a Quant Data & AI Platform Engineer to own the data backbone of Algocor’s quant and AI infrastructure.
Algocor AI is building a trading system where research, execution, market data, and an LLM-based agent layer operate on the same infrastructure. This creates a technically demanding yet highly meaningful engineering challenge: building a data layer that is reliable, validated, observable, and ready for consumption by both quant systems and AI agents.
This is not a generic AI role, and it is not a traditional data engineering position hidden inside a large team.
It is a hands-on platform engineering role with clear ownership: designing and building the pipelines, APIs, schemas, validation logic, and monitoring layer that the rest of the system will rely on.
If you enjoy working at the intersection of market data, time-series systems, production-grade Python, APIs, and AI-facing infrastructure, this role is likely to feel close to the kind of problems you want to solve.
What You’ll Build
You will design and run the pipelines, APIs, and data infrastructure that connect external market-data providers, Algocor’s on-prem quant systems, and its cloud-based AI layer.
Your work will include:
Building and maintaining ETL/ELT pipelines from market-data providers such as Pyth, Databento, exchange APIs, and broker APIs into the on-prem stack
Designing the boundary between on-prem systems, such as QuestDB and kdb+, and the cloud environment on AWS
Deciding what gets calculated where, how data is synchronized, and how sync health is monitored
Writing production-grade Python APIs with FastAPI to expose clean, validated tables to the AI layer
Owning the symbol master, data schemas, validation rules, and sync monitoring across environments
Building agent-facing data tools such as query interfaces, document retrieval flows, and dataset endpoints
Implementing data quality checks, observability, and alerting for ingestion and sync health
Documenting datasets, schemas, access rules, and assumptions so the rest of the team can build confidently on your work
This is a role where you will be expected to scope, build, ship, monitor, and improve the systems you own.
Why This Role Matters
The AI layer is only as reliable as the data infrastructure underneath it.
In this role, your work will directly shape how confidently the team can use data across quant research, execution systems, internal tooling, and AI agents. You will not be far away from the actual technical problem; you will be working close to the architecture, the data, and the people building on top of it.
You will have:
End-to-end ownership of the data layer
Direct collaboration with the AI lead on architecture
A modern stack with real engineering problems
A small, focused team where individual contribution is visible
The opportunity to build infrastructure that directly supports quant and AI systems
A hybrid working model based on Büdotek Teknopark, Dudullu
Competitive compensation based on experience
What We’re Looking For
We are looking for an engineer with strong backend and data engineering fundamentals, and the judgment to build systems that other people and other systems can rely on.
You do not need to have worked with every tool in Algocor’s stack. What matters is that you have built production-grade systems, understand data quality and reliability, and can reason clearly about trade-offs in data infrastructure.
You are likely to be a strong fit if you have:
Strong Python experience
You write tested, maintainable, production-grade Python code that other people can build on.
SQL and database design experience
You are strong in SQL and relational database design, with solid PostgreSQL experience.
Time-series or analytical data exposure
You have worked with at least one time-series or analytical database such as QuestDB, kdb+, ClickHouse, TimescaleDB, or a similar system.
API development experience
You have worked with FastAPI or an equivalent API framework, and you are comfortable with async patterns and WebSockets.
Data engineering fundamentals
You understand ETL/ELT, schema design, validation, orchestration, data quality, and monitoring.
Production ownership mindset
You are comfortable with Docker, Linux, Git, and CI/CD. You care about observability, documentation, reliability, and what happens after something is shipped.
Clear technical communication
You can take a research, business, or product need and turn it into a practical technical specification without overcomplicating the process.
What This Role Is Not
This is not a prompt engineering role.
It is not a generic AI role.
It is not pure ML research.
This is a data and platform engineering role where the AI layer is the consumer of your work, not the main focus of it.
If your main interest is fine-tuning models or building AI demos, this may not be the right role. But if you believe that AI systems are only as strong as the data layer underneath them — and you want to build that layer properly — this role may be a strong match.
Location
Büdotek Teknopark, Dudullu — İstanbul