GenAI Specialist

What matters most

• Select use cases with measurable value

  Partner with business leaders and teams across Digital, IT, Engineering, Operations, Supply Chain and Finance to prioritize opportunities, translate problems into practical AI solutions, and track ROI and productivity gains.

• Turn GenAI concepts into working solutions

  Rapidly validate proofs of concept, then design and deploy copilots, agents, multi-agent workflows and retrieval-augmented generation solutions using enterprise knowledge.

• Integrate with enterprise systems

  Use the stated Microsoft AI technologies and connect solutions to Microsoft 365, SharePoint, ERP and other applications through APIs, microservices and reusable components.

• Own secure, scalable deployment

  Carry solutions from ideation through deployment and improvement while supporting governance, security, compliance, Responsible AI and enterprise architecture standards.

• Drive adoption and sustained value

  Educate stakeholders, build user trust, monitor performance and adoption, and develop reusable assets. The posting defines success in terms of widely adopted solutions and measurable business outcomes.

Likely challenges to think through

• Speed versus production safeguards

  Preparation hypothesis: rapid POCs may create pressure to scale before security, governance and maintainability are ready. Prepare to explain decision gates for validating value, assessing risk and hardening a solution before wider deployment.

• Useful answers from complex enterprise data

  Preparation hypothesis: connecting knowledge sources and business applications may require balancing answer relevance against access controls and integration complexity. Prepare an approach to source selection, permissions, retrieval evaluation and monitoring.

• Adoption versus demonstrated impact

  Preparation hypothesis: usage alone may not establish productivity gains or ROI across different functions. Prepare to distinguish adoption measures from outcome measures and explain how stakeholder feedback would guide improvements.

Topics to prepare

• LLMs and enterprise RAG

  The posting requires LLMs, prompt engineering, embeddings, context management, and RAG over enterprise knowledge. Revise by sketching how a document becomes retrievable context, then explain how you would check whether an answer is supported by its source.

• Agentic and multi-agent design

  Autoliv explicitly seeks copilots, intelligent agents, and multi-agent systems. Revise by diagramming an illustrative business workflow, identifying each agent’s responsibility, the orchestration steps, and where a person should review a decision.

• Microsoft AI ecosystem

  The role names Azure OpenAI Service, Copilot Studio, Semantic Kernel, Azure AI Studio, Microsoft Fabric, and Dataverse. Revise by drawing a possible solution architecture and explaining the purpose of each named component you choose to include; the architecture is a preparation exercise, not a prescribed company design.

• Use-case selection and value realization

  The role spans business-led prioritization, POCs, adoption, ROI, and productivity gains. Revise by comparing two hypothetical use cases on expected value, feasibility, and measurable outcomes, then defining a POC decision point and an adoption measure.

• Governance and solution lifecycle

  The posting calls for security, compliance, Responsible AI, monitoring, and ownership through deployment and improvement. Revise by listing risks and review points for a hypothetical enterprise copilot, then identifying what you would monitor after launch.

Skills to revise

• Prototype an enterprise-knowledge RAG flow

  Practice assembling a small example with sample documents, embeddings, retrieved context, and an LLM prompt; test questions that the documents can and cannot answer. This directly rehearses the posted RAG and context-management work.

• Build a controlled agent workflow

  Practice implementing a small agent or multi-agent workflow using Semantic Kernel, with explicit steps and a review point before an action. This targets the stated agentic development and orchestration responsibilities.

• Develop a Python service interface

  Practice exposing a sample copilot or RAG function through a Python REST API, including clear input and output handling. The posting explicitly requires strong Python, APIs, and microservices skills for scalable deployments.

• Plan an enterprise integration

  Sketch and, if access permits, prototype how a solution would exchange data with SharePoint, Microsoft 365, an ERP system, or Dataverse. Note data boundaries and failure handling. These platforms are named in the integration requirements; the sample design is only a revision exercise.

• Exercise deployment and monitoring

  Package a sample service with Docker, outline an Azure CI/CD path, and specify checks for solution performance and usage. Docker, Azure, CI/CD, LLMOps, and monitoring appear in the posting; this exercise connects a POC to sustained operation.

• Present a POC decision to stakeholders

  Practice a short briefing that translates a hypothetical Operations or Supply Chain problem into a POC scope, success measures, risks, and a scale-or-stop recommendation. This rehearses the role’s cross-functional communication and business-value responsibilities.

• Draft a use-case prioritization brief

  The role calls for partnering with business leaders across functions. As a preparation exercise, compare two possible GenAI use cases by business value, feasibility, data access, risks and measurable outcomes; do not present them as Autoliv projects.

• Prepare an end-to-end project walkthrough

  Select a real AI, automation or data project you can discuss. Summarize the business problem, your contribution, proof-of-concept decision, deployment, adoption and measured results; distinguish actual results from estimates.

• Sketch a RAG and agent design

  Prepare a design exercise using an enterprise knowledge source. Show retrieval, embeddings, prompts, context management and evaluation, then explain when an agent or multi-agent approach would add value over a simpler workflow.

• Diagram integration and deployment choices

  Sketch how a Python API or microservice could connect a GenAI solution to Microsoft 365, SharePoint or an ERP system. Annotate relevant Azure and Microsoft AI components, data flows, Docker and CI/CD choices, and what you would need to validate.

• Prepare a governance and measurement plan

  The posting emphasizes security, Responsible AI, monitoring and value realization. Draft checks for access, data handling, output quality and ongoing performance, plus proposed adoption, productivity and ROI measures.

• Organize evidence against the requirements

  Make a one-page index of examples you can substantiate for GenAI, RAG, agents, Microsoft AI tools, integrations and cross-functional delivery. Separately note evidence for the stated degree and six-year experience requirements; list any relevant certifications only if held, since they are preferred.

• Write questions for the employer

  Prepare questions about how use cases are prioritized, which enterprise knowledge sources and platforms are in scope, who owns governance decisions, and how the team judges a POC ready to scale.