Applied AI Engineer

Required Skills

• Strong experience withPython for AI, machine learning, data science, and application development
• Strong background indata science, including data preparation, feature engineering, statistical analysis, experimentation, and model evaluation
• Hands-on experience developing and deployingmachine learning models
• Experience with common ML frameworks and libraries such as:
  • PyTorch
  • TensorFlow
  • scikit-learn
  • pandas
  • NumPy
• Strong experience developingGenerative AI and LLM-based applications
• Hands-on experience buildingagentic AI solutions, including:
  • AI agents
  • Tool/function calling
  • Multi-step workflows
  • Agent orchestration
  • State and memory management
  • Human-in-the-loop patterns
  • Guardrails and controlled agent actions
• Experience withRetrieval-Augmented Generation (RAG)
• Experience with embeddings, semantic search, vector databases, and enterprise knowledge retrieval
• Strong understanding ofprompt engineering, context engineering, and structured model outputs
• Experience evaluating AI systems for:
  • Accuracy
  • Reliability
  • Hallucination
  • Latency
  • Cost
  • Safety
  • Business effectiveness
• Fluent or professional working proficiency inEnglish is required for written documentation, technical discussions, collaboration, and stakeholder communication.

Enterprise AI Integration

• Experience integrating AI solutions withenterprise APIs, databases, messaging systems, and business applications
• Understanding of synchronous and asynchronous integration patterns
• Ability to design AI agents that securely interact with enterprise systems without providing unrestricted access
• Experience working withstructured and unstructured enterprise data
• Strong understanding of SQL, relational database structures, and application data models
• Experience withPostgreSQL or similar relational databases
• Understanding of caching and state-management architectures for AI applications
• Knowledge of event-driven architectures and message-based processing

Azure & Cloud Experience

• Azure OpenAI
• Azure AI services and AI development tooling
• Azure API Management
• Azure Service Bus
• Azure Event Hubs
• Azure Database for PostgreSQL
• Microsoft Entra ID
• Identity and Access Management
• Azure Key Vault
• Azure Monitor and Application Insights
• Containers and cloud-native deployment
• CI/CD and automated deployment pipelines

Machine Learning & Data Science Knowledge

• Supervised and unsupervised learning
• Classification, regression, clustering, and ranking techniques
• Model selection and validation
• Feature engineering
• Statistical analysis and experimental design
• Model performance measurement
• Data quality assessment
• Model drift and production monitoring
• ML lifecycle and MLOps principles
• Ability to determine when traditional machine learning is more appropriate than an LLM-based solution

AI Engineering & Agentic Development

• Experience with AI orchestration frameworks or SDKs used to build agentic systems
• Understanding ofsingle-agent and multi-agent architectures
• Experience designing tool interfaces that allow AI systems to safely interact with APIs and enterprise applications
• Understanding of deterministic versus probabilistic application components
• Experience implementingguardrails, validation, retries, approval workflows, and human oversight
• Knowledge of AI observability, tracing, evaluation, and debugging
• Understanding of token consumption, model latency, model selection, and AI cost optimization
• Experience designing AI systems that operate reliably within established business rules and constraints

Industry & Business Experience

• Experience developing AI, machine learning, analytics, or software solutions forconsumer goods, manufacturing, supply chain, distribution, or related industries
• Ability to understand business processes and translate operational problems into practical AI solutions
• Experience working with enterprise data such as:
  • Product data
  • Order data
  • Customer data
  • Manufacturing data
  • Supply-chain data
  • Master data
• Understanding of enterprise systems such as ERP, MES, WMS, order-management, or shop-floor platforms

Architecture & Engineering Knowledge

• Strong understanding ofREST APIs and enterprise integration patterns
• Familiarity with microservices and distributed application architectures
• Understanding of authentication, authorization, and secure access to enterprise data
• Knowledge of resiliency, fault handling, retry strategies, observability, and production support
• Ability to design AI solutions that can be integrated into existing enterprise applications rather than operating only as standalone prototypes

Preferred Experience

• Experience withSAP, SAP S/4HANA, SAP BTP, SAP Integration Suite/CPI, SAP MDG, or SAP-related data
• Experience applying AI or machine learning tomanufacturing, product configuration, order management, forecasting, quality, supply chain, or operational processes
• Experience withMLOps and LLMOps
• Experience with vector search platforms and AI knowledge-retrieval architectures
• Experience with distributed caching technologies such as Redis
• Experience with AI evaluation and observability frameworks
• Experience using AI-assisted software development tools such asOpenAI Codex, Claude Code, or GitHub Copilot
• Experience taking AI solutions from proof of concept throughproduction deployment and operational support

Key Characteristics

• Strong analytical and problem-solving skills
• Comfortable working across data science, software engineering, and enterprise architecture
• Able to experiment rapidly while maintaining production engineering discipline
• Able to explain AI concepts, limitations, risks, and results to both technical and business stakeholders
• Strong focus on solving measurable business problems rather than implementing AI for its own sake