Full Description

Agentic Data Engineering & AI Automation Engineer

Location: Basel, Switzerland — On-site

Employment Type: Full-time, Fixed-term Project

Experience: Minimum 5 years; ideally 5–7 years

Project Start: 1 December 2026

Project End: 30 June 2027

Project Duration: 7 months Company Description Potniya is a Swiss technology services company delivering solutions across data engineering, AI and intelligent automation, cloud and infrastructure, cybersecurity, and enterprise technology. We help organizations modernize their data platforms, automate operational processes, and build secure, reliable and scalable technology solutions. Our work combines engineering expertise with a strong focus on reliability, security, governance and measurable business outcomes. Role Description Potniya is looking for an Agentic Data Engineering & AI Automation Engineer to join our team in Basel for a seven-month project running from 1 December 2026 to 30 June 2027. This role sits at the intersection of data engineering, AI agents, backend engineering and workflow automation. You will design and build production-oriented systems that integrate data pipelines with intelligent automation — enabling automated investigation, evidence collection, decision support, operational monitoring and controlled remediation workflows. The role involves hands-on engineering with Python, SQL, Apache Airflow, FastAPI, dbt and PostgreSQL, together with AI/LLM components, APIs and modern data platforms. This is an engineering-focused position. The objective is not simply to build AI prototypes, but to integrate AI and agentic capabilities into reliable, observable, secure and governed production systems. Key Responsibilities Design, develop and maintain reliable data pipelines and automated workflows. Build agentic workflows for automated investigation, evidence collection, decision support and controlled remediation. Develop backend services and APIs using Python and FastAPI. Design and orchestrate production workflows using Apache Airflow. Develop and integrate dbt models and transformation workflows. Integrate dbt metadata, execution results and operational evidence into automated investigation processes. Design data persistence, incident tracking and evidence-management components using PostgreSQL. Integrate LLMs and AI agents with enterprise data, APIs and operational systems. Develop tool-calling and multi-step agentic workflows. Implement appropriate human-in-the-loop and approval mechanisms for controlled automated actions. Implement monitoring, logging, observability, retries, failure handling and audit trails across automated workflows. Develop automated tests and validation mechanisms for data and AI components. Support deployment, CI/CD, containerization and production operation of data and AI services. Troubleshoot complex issues across data pipelines, APIs, databases and automated workflows. Collaborate with data, software, cloud, AI and security teams throughout solution design and delivery. Produce clear technical documentation covering architecture, workflows, interfaces and operational procedures. Qualifications & Experience Minimum 5 years of relevant professional experience; ideally 5–7 years, in data engineering, backend/software engineering, AI engineering, data platforms or closely related technical roles. Strong professional experience with Python and SQL. Hands-on experience designing, building and operating production-grade data pipelines. Experience with workflow orchestration technologies, preferably Apache Airflow. Practical experience with dbt and modern ELT/data transformation workflows. Experience developing REST APIs and backend services; FastAPI experience is highly desirable. Strong experience with relational databases, preferably PostgreSQL. Experience with modern cloud and data-platform architectures. Practical experience with LLMs, AI agents, tool calling, RAG or AI-driven automation is highly desirable. Understanding of production engineering concepts including observability, monitoring, retries, idempotency, failure handling and testing. Familiarity with Git, CI/CD, containers and modern software development practices. Understanding of security, access control, data governance and auditability in enterprise environments. Strong analytical, troubleshooting and problem-solving skills. Ability to work independently while collaborating effectively with technical and business stakeholders. Education Master’s degree or higher in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Information Systems, or another relevant technical discipline is required. PhD candidates and PhD graduates are also welcome to apply. Current Master’s students may be considered if they are close to graduation and meet the required professional experience criteria. Degrees from Swiss universities, Universities of Applied Sciences (FH/UAS), ETH/EPFL, and recognized international universities are accepted.

Application Documents Please submit your CV when applying. Shortlisted candidates will be asked to provide a copy of their Master’s degree (or higher qualification) and the corresponding academic transcript / grade record as part of the selection process.

Languages Fluent in German and English is required.

Contract & Project Duration This is a full-time, fixed-term project position based on-site in Basel, Switzerland.

Start date: 1 December 2026

End date: 30 June 2027

Duration: 7 months The position is initially tied to this project. Depending on project development, business requirements and mutual fit, further collaboration may be possible after completion of the initial project.