Arbetsbeskrivning

Assignment Description:

We are looking for a Senior AI agent engineer who is enthusiastic about making real business impact with AI agents in autonomous transportation. We have identified and validated four use cases to implement in 2026. All use cases have been developed with clear team ownership and commitment from management. Use cases in 2026 focus on well-defined workflows in software development, customer service and operational support and will include internal data and system integration via MCP and OpenAI-based platform for LLM capabilities.

The ideal candidate has a proven ability in implementing agentic ai solutions and have a passion for bringing the latest technologies to the market. Also, the candidate has strong communication and change management skills and know how to lead cross functional AI initiatives with focus on tangible business outcomes.

Your Responsibilities

• Drive the implementation of Agentic AI in the ATS offboard system domain by investigating and experimenting on relevant use cases in relation to process automation and workflow orchestration.
• Coach and contribute as though leader within AI engineering through cross-functional collaboration and knowledge sharing. 
• Drive operation development scenarios within autonomous transport systems with advanced data-driven methods
• Developing software and services as well as designing algorithms for data analysis within autonomous transport system domain
• Develop and integrate AI solutions that enhance the impact of our operational data products
• Helping the team to design and develop appropriate data pipelines in Kafka and Databricks

Requirement:

• Master’s or Bachelor’s degree in Data Analytics, Computer Science, Information Technology or another related fields.
• 2-5 years of proven work experience from working with Agentic AI.
• 5 years of proven work experience as software developer.

Competence profile

Main:

• Solid experience of building AI agents and understanding of platforms like OpenAI Agent builder

• Solid theoretical background in Machine Learning, Big Data Analytics and Statistics.

• Knowledge and experience in Machine Learning and Data Analytics tools such as Databricks, Apache Spark, TensorFlow etc. Can design, develop, and maintain scalable data pipelines and solutions using Databricks, integrating with AWS, to process and analyse large datasets for business insights.

• Solid experience in Python (with focus on Machine Learning and Data Analytics)

Supporting:

• Solid experience in AWS (Amazon Web Services) Cloud Computing Platform. Experience in Other cloud computing platforms (e.g. Microsoft Azure) is a plus.

• Good experience in Apache Kafka (Stream Processing Platform) and Event-Driven Systems

• Demonstrated experience in Alarming, Monitoring and Logging for Event-driven Systems.

• Knowledge and experience in Terraform (Infrastructure as Code tool for cloud)

• Knowledge and experience in CI/CD and GitLab

Use cases that the candidate will be developing:

1. Operations Support – Tiered Support Automation

This use case supports the Tier 1/2 operations team by helping with ticket triage. The idea is straightforward: when a new ticket arrives, the system classifies it, pulls in relevant context (logs, metrics, similar past tickets, known fixes), and presents a “ready-to-act” view so humans can resolve or escalate faster.

2. Road Safety – Remote Assistance Triage

Remote Assistance Triage targets roadside incidents, where supervisors must very quickly understand what is happening and decide the next step. The idea is that the system ingests vehicle/telemetry logs and related data, retrieves similar past degradations and resolutions, and proposes likely causes and next actions, together with links to the underlying evidence.

3. Support for Test Report – Autogen & Live Status

This use case is about automating test campaign reporting. Instead of engineers manually pulling numbers and screenshots from multiple tools into Excel or slides, a workflow would assemble the report directly from source systems and keep it up to date, with traceable references to underlying results and issues.

4. Support for Test Analysis – Coverage Intelligence

Here the goal is to support test engineers and safety reviewers in understanding what has actually been tested and where the gaps are. The system would link requirements, planned tests, and executed tests, then highlight missing coverage, redundant tests, and weak traceability.