AI / ML Engineer

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 teams
• Collaborates often with other teams to support the business goals of the department, function, and domain of expertise; recognized by leaders, DPOs, DPMs from different teams
• Influences scope across solutions/processes/deliveries
• Is up to date on evolving standards, platforms, and technologies within the domain
• Demonstrates the ability to work and contribute in various areas of the team's scope
• Actively 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 develop
• Works closely with stakeholders (e.g. DPOs/DPAOs and DPMs) to plan and execute deliverables; takes the lead on non-trivial initiatives to address these needs
• Helps to identify key opportunities and enhancements that will drive significant value for end users/stakeholders
• Participates in networks/fora/groups
• Works within ambiguity and takes on challenging roles and tasks outside main assignments when needed
• Drives end-to-end scope across teams, understanding the whole picture
• Dedicates 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 decisions
• Strong evaluation methodology: precision/recall optimization, threshold tuning, confusion matrix analysis, drift detection, A/B testing, online evaluation, LLM-as-judge/rubric-based evaluation
• Solid understanding of vector search, embedding models, and retrieval-augmented generation (RAG)
• Knowledge Graph, Context Graphs, MCP Server Development
• Experience with MLOps/LLMOps: experiment tracking, model/prompt versioning, model registry, deployment, monitoring, rollback
• Strong understanding of observability for AI systems: latency, quality, drift, cost, and failure analysis
• Traditional ML fundamentals: feature engineering, model selection, calibration, interpretability
• Understanding of MLOps, LLMOps or AI application lifecycle management.

Data Engineering

• Advanced Python skills (3.11+): async patterns, Pydantic, type-safe data modeling, performance optimization
• Production experience with a data platform (Databricks, Snowflake, or similar)
• Proven track record building ETL/ELT pipelines that handle structured data at scale and under SLA
• Experience 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 experience
• Experience with distributed processing frameworks such as Spark/PySpark
• Familiarity with batch and streaming data architectures
• Data governance experience: lineage, access control, PII handling, security, compliance

Software Engineering

• Expert-level Git workflows, CI/CD pipeline design, and code review leadership
• Writes production-grade code: well-tested, well-documented, maintainable
• Designs and consumes REST APIs; understands service architecture patterns
• Comfortable owning and evolving a large production codebase
• Experience with Docker, Kubernetes, and Infrastructure as Code (Terraform or equivalent)
• Strong production debugging, reliability engineering, and performance tuning skills
• Experience with secrets/configuration management and secure software delivery

Nice to Have

• Experience with LLM observability platforms (Arize, Langfuse, LangSmith) in production
• Domain expertise in procurement, supply chain, finance, or operational workflows
• Experience with Azure OpenAI or other enterprise LLM providers at scale
• Track record of measuring and communicating business impact (cost savings, time reduction, accuracy gains)
• Infrastructure experience: Docker, Kubernetes, Terraform, or similar IaC tools
• Experience 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 scale
• Technical leadership: Shape architecture, set standards, and influence product direction
• End-to-end ownership: From data strategy through LLM pipeline to production monitoring
• Small, 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.