AI / ML Engineer

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.

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Who We Are And What We Believe In

We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.

Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.

Trucks Technology & Industrial Division hire team players who are ready to create real customer impact. Our decentralized teams work close to our customers, with speed and autonomy, to build what they truly need.

Join us to collaborate on innovative, sustainable technologies that redefine how we design, build, and deliver value. Bring your curiosity, your expertise, and your collaborative energy, and together, we’ll turn bold ideas into tangible solutions for our customers and contribute to a more sustainable tomorrow.

Resource Profile: AI/ML Engineer

We are building a dedicated AI Hub for Purchasing to accelerate the adoption and scale of Artificial Intelligence across the Procurement organization.

As an AI/ML Engineer, you will be a core technical member of this team, responsible for turning high-value Purchasing opportunities into scalable, secure and production-ready AI solutions.

You will work at the intersection of Generative AI, Machine Learning, data, software engineering and enterprise architecture, designing and building solutions that fundamentally change how Purchasing operates.

This is not a role focused solely on developing machine-learning models. You will work across the full AI lifecycle—from experimentation and prototyping through architecture, engineering, deployment, evaluation and continuous improvement.

You will help shape how AI is applied across areas such as strategic sourcing, supplier intelligence, spend management, contract intelligence, purchasing operations, supplier risk and decision support.

Impact & Expectations

Is the go-to person for my own team on the full scope of the delivery and a strong contributor cross teamsCollaborates often with other teams to support the business goals of the department, function, and domain of expertise; recognized by leaders, DPOs, DPMs from different teamsInfluences scope across solutions/processes/deliveriesIs up to date on evolving standards, platforms, and technologies within the domainDemonstrates the ability to work and contribute in various areas of the team's scopeActively works on the competence development of the team, contributes to and shares best practices in the community (practitioners group, department, etc.)Dedicates time to activities that support upskilling of colleagues and consistently helps new hires and more junior team members to developWorks closely with stakeholders (e.g. DPOs/DPAOs and DPMs) to plan and execute deliverables; takes the lead on non-trivial initiatives to address these needsHelps to identify key opportunities and enhancements that will drive significant value for end users/stakeholdersParticipates in networks/fora/groupsWorks within ambiguity and takes on challenging roles and tasks outside main assignments when neededDrives end-to-end scope across teams, understanding the whole pictureDedicates time to activities contributing to the local community

Key Responsibilities

Design & Build AI Solutions

Translate complex Purchasing business challenges into technically robust AI solutions.Design and develop AI/ML applications using Generative AI, Large Language Models (LLMs), traditional ML and emerging AI technologies.Build intelligent applications including AI assistants, copilots, autonomous/semi-autonomous agents and AI-powered workflows.Develop solutions using APIs, model orchestration, tool calling and enterprise system integrations.Rapidly prototype new AI capabilities while maintaining a clear path toward production deployment.

GenAI, RAG & Knowledge Engineering

Design and implement Retrieval-Augmented Generation (RAG) solutions using structured and unstructured enterprise data.Work with vector databases, embeddings, semantic search and hybrid retrieval.Design and develop knowledge graphs, context graphs and semantic/knowledge layers where appropriate.Develop approaches for connecting relationships across suppliers, categories, contracts, products, locations, transactions and external intelligence.Explore and implement Graph RAG and other advanced retrieval and reasoning architectures.Develop robust approaches to context engineering, grounding and hallucination reduction.

Machine Learning & Advanced AI

Develop and deploy machine-learning models for relevant Purchasing use cases such as supplier risk, forecasting, classification, anomaly detection, recommendation and optimization.Select appropriate algorithms and AI approaches based on the problem rather than defaulting to a particular technology.Design experiments and evaluate model performance using appropriate quantitative and qualitative metrics.Apply statistical and machine-learning techniques to extract value from complex enterprise data.

Production Engineering & MLOps

Take AI solutions from prototype to production.Build scalable, reliable and maintainable AI services and applications.Implement model and prompt evaluation, monitoring, observability and performance management.Establish appropriate approaches for model/version management, testing, deployment, rollback and continuous improvement.Design solutions for scalability, latency, reliability and cost efficiency.Work with cloud platforms and enterprise AI infrastructure to operationalize AI at scale.

Data & Enterprise Integration

Work closely with Data Engineers and Enterprise Architects to access and integrate relevant Purchasing data.Integrate AI solutions with enterprise platforms such as ERP, procurement, supplier, contract and data platforms.Work with both structured and unstructured data.Contribute to data preparation, feature engineering, data quality and AI-ready data architectures.Design APIs and interfaces that enable AI solutions to interact with enterprise applications and business processes.

Responsible & Secure AI

Build AI solutions with security, privacy, responsible AI and enterprise controls embedded from the outset.Implement appropriate controls around sensitive commercial, supplier and business information.Contribute to AI evaluation frameworks covering accuracy, robustness, bias, safety, explainability and reliability.Partner with Cybersecurity, Data Privacy, Legal, Risk and Architecture teams to ensure solutions meet enterprise standards.

Technology Leadership & Innovation

Stay current with rapidly evolving developments in GenAI, LLMs, AI agents, ML, knowledge graphs and AI engineering.Evaluate emerging models, frameworks, platforms and technologies and determine their relevance to Purchasing.Establish reusable AI components, patterns and accelerators that can be leveraged across multiple use cases.Contribute to the technical standards, architecture patterns and engineering practices of the Purchasing AI Hub.Mentor other engineers and contribute to building the organization's AI engineering capability.

Required Skills

Data Science & ML

Extensive hands-on experience with LLMs in production (prompt engineering, prompt/version management, structured output, tool use, agent patterns, guardrails, hallucination mitigation, human-in-the-loop workflows)Minimum 3–5 years of hands-on experience designing, building and deploying production-grade AI solutions.Deep knowledge of LLM orchestration frameworks (LangChain, LangGraph, or equivalent)Proven ability to design ensemble/voting strategies for robust, auditable AI decisionsStrong evaluation methodology: precision/recall optimization, threshold tuning, confusion matrix analysis, drift detection, A/B testing, online evaluation, LLM-as-judge/rubric-based evaluationSolid understanding of vector search, embedding models, and retrieval-augmented generation (RAG)Knowledge Graph, Context Graphs, MCP Server DevelopmentExperience with MLOps/LLMOps: experiment tracking, model/prompt versioning, model registry, deployment, monitoring, rollbackStrong understanding of observability for AI systems: latency, quality, drift, cost, and failure analysisTraditional ML fundamentals: feature engineering, model selection, calibration, interpretabilityUnderstanding of MLOps, LLMOps or AI application lifecycle management.

Data Engineering

Advanced Python skills (3.11+): async patterns, Pydantic, type-safe data modeling, performance optimizationProduction experience with a data platform (Databricks, Snowflake, or similar)Proven track record building ETL/ELT pipelines that handle structured data at scale and under SLAExperience designing data validation and quality frameworks (pandera, Great Expectations, or custom)Strong cloud experience (Azure preferred; AWS or GCP acceptable)Strong SQL skills and data modeling experienceExperience with distributed processing frameworks such as Spark/PySparkFamiliarity with batch and streaming data architecturesData governance experience: lineage, access control, PII handling, security, compliance

Software Engineering

Expert-level Git workflows, CI/CD pipeline design, and code review leadershipWrites production-grade code: well-tested, well-documented, maintainableDesigns and consumes REST APIs; understands service architecture patternsComfortable owning and evolving a large production codebaseExperience with Docker, Kubernetes, and Infrastructure as Code (Terraform or equivalent)Strong production debugging, reliability engineering, and performance tuning skillsExperience with secrets/configuration management and secure software delivery

Nice to Have

Experience with LLM observability platforms (Arize, Langfuse, LangSmith) in productionDomain expertise in procurement, supply chain, finance, or operational workflowsExperience with Azure OpenAI or other enterprise LLM providers at scaleTrack record of measuring and communicating business impact (cost savings, time reduction, accuracy gains)Infrastructure experience: Docker, Kubernetes, Terraform, or similar IaC toolsExperience leading technical decisions in a team without heavy management overhead

What We Offer

High-impact production system: Not a research project - your work directly drives business value at scaleTechnical leadership: Shape architecture, set standards, and influence product directionEnd-to-end ownership: From data strategy through LLM pipeline to production monitoringSmall, autonomous team: No layers of approval - ship weekly, measure impact, iterate fast

Who You Are

Minimum 3–5 years of hands-on experience designing, building and deploying production-grade AI solutions.