Senior Software Engineer (Data & AI)

Job description

We are looking for a Senior Software Engineer (Data & AI) to design, build, and scale production AI applications and agentic systems that solve complex operational and analytical challenges across foodpanda’s footprint in APAC and Europe. In this role, applied AI takes center stage—grounded by robust data infrastructure. While your primary focus will be architecting agentic workflows, advanced RAG systems, and intelligent automations, you will also design, build, and maintain the underlying data pipelines that feed these systems.

You will take full end-to-end ownership of the stack: ensuring data quality and pipeline performance, orchestrating agent behaviors, and actively participating in the team’s production on-call rotation to keep our services reliable and resilient. An initiative within the team is our agentic analytics platform , which connects AI agents directly with our enterprise business logic, semantic layers, and context engines to deliver accurate, verifiable insights.

Responsibilities

Deliver End-to-End AI Applications: Design, build, and maintain production AI applications, including agentic workflows, RAG systems, and integrations with business knowledge, semantic layers, and external tools. Build solutions that go beyond prototypes and deliver measurable value to users.

Practice Evaluation-Driven Development

Build and maintain evaluations to measure the correctness, reliability, and effectiveness of AI applications. Leverage evaluation datasets, automated testing, production observability, and user feedback to understand failure modes and continuously improve system behavior.

Iterate Closely with Stakeholders

Partner directly with analytics and business teams to understand their workflows, challenge assumptions, and translate ambiguous problems into practical solutions. Rapidly prototype, validate ideas, and refine products based on real-world feedback.

Own Software End-to-End

Take ownership across the software lifecycle, from technical discovery and architecture to implementation, testing, deployment, monitoring, and continuous improvement. Be comfortable working across data infrastructure, backend services, and user-facing applications where needed.

Balance Speed with Engineering Discipline

Leverage modern development tools, including AI coding agents, to accelerate delivery while maintaining high standards for software design, automated verification, security, and maintainability.

Contribute to Engineering Excellence

Drive technical design discussions, contribute to architectural decisions and RFCs, participate in code reviews and pair programming, and mentor other engineers through knowledge sharing and collaboration.

Maintain Production Reliability

Take responsibility for the systems you build and maintain, including observability, incident investigation, performance optimization, and participation in the team’s on-call rotation.

Build and Own Data & Context Pipelines

Design, build, and maintain the ingestion, ETL/ELT, and context-retrieval pipelines that feed our AI models, semantic layers, and agentic workflows. Take shared ownership of pipeline reliability, data freshness, and incident response as an active participant in the team's production on-call rotation

Qualifications

Strong Software Engineering Fundamentals

You have 7+ years of software engineering experience, with strong proficiency in Python, SQL, system design, and automated testing. You enjoy building reliable, maintainable production systems.

Applied AI Experience

You’ve developed LLM-powered applications, agentic workflows, or related systems, and understand the challenges of taking AI solutions from experimentation into production.

AI Evaluation & Observability

You’ve built evaluation datasets, frameworks, or automated harnesses to systematically measure and improve AI application performance, or have experience with observability tools such as Langfuse.

Engineering and Product Judgment

You have informed opinions about how AI systems should behave. You know when to use LLMs, when deterministic software is more appropriate, and how to balance correctness, complexity, cost, and user experience.

Curiosity and Technical Awareness

You stay informed about emerging AI research, models, agent architectures, and software engineering practices. You enjoy experimenting with new ideas while remaining pragmatic about what delivers value.

Ownership and Product Mindset

You’re comfortable navigating ambiguity, working directly with stakeholders, and making thoughtful decisions when there isn’t a single right answer. You care about the experience of the people using your software.

Pragmatic Engineering Approach

You enjoy shipping quickly to learn, but don’t compromise user trust, software reliability, or maintainability in the process. You value evidence, testing, and continuous improvement.

Collaborative Mindset

You enjoy working with other engineers through technical discussions, pair programming, code reviews, and mentorship.

Good to have: Hands-on Data Engineering Experience: You’ve built and maintained production data pipelines, data integrations, or data-intensive systems, and understand the importance of data quality, modeling, and operational reliability.

Modern Data Platforms

You’ve worked with technologies such as BigQuery, dbt, Apache Airflow, PySpark, or lake house architectures.

Knowledge & Semantic Systems

You’ve built applications integrating LLMs with structured data, semantic layers (dbtflow, cube), knowledge graphs, context engineering, or retrieval systems.

Cloud & DevOps

You have experience with AWS or GCP, Docker, and automated CI/CD pipelines.

Full-Stack Product Development

You’ve shipped product features end-to-end and are comfortable working across frontend, backend, and model integrations.

Developer Experience

You’ve built internal platforms, developer tools, or infrastructure that improve how other engineers build, test, or operate software.