Applied AI Engineer

We are seeking an Applied AI Engineer to design, build, evaluate, and deploy AI-enabled solutions for business-critical applications in a consumer goods and manufacturing environment.
This role will work within an enterprise Microsoft Azure architecture that includes API Management, Service Bus, Event Hubs, PostgreSQL databases, enterprise integrations, identity and security services, and connections to systems such as SAP, ordering platforms, and shop-floor management systems.
The ideal candidate combines a strong foundation in data science and machine learning with hands-on experience building production AI applications, AI agents, and enterprise AI integrations.

Required Skills

• Strong experience with Python for AI, machine learning, data science, and application development
• Strong background in data science, including data preparation, feature engineering, statistical analysis, experimentation, and model evaluation
• Hands-on experience developing and deploying machine learning models
• Experience with common ML frameworks and libraries such as:
  • PyTorch
  • TensorFlow
  • scikit-learn
  • pandas
  • NumPy
• Strong experience developing Generative AI and LLM-based applications
• Hands-on experience building agentic 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 with Retrieval-Augmented Generation (RAG)
• Experience with embeddings, semantic search, vector databases, and enterprise knowledge retrieval
• Strong understanding of prompt 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 in English is required for written documentation, technical discussions, collaboration, and stakeholder communication.

Enterprise AI Integration

• Experience integrating AI solutions with enterprise 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 with structured and unstructured enterprise data
• Strong understanding of SQL, relational database structures, and application data models
• Experience with PostgreSQL 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

Experience building and deploying AI solutions within Microsoft Azure, with familiarity with technologies such as:

• 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

The candidate should be able to work effectively with cloud and platform engineering teams to move AI solutions from experimentation into secure, scalable production environments.

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 of single-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 implementing guardrails, 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 for consumer 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 of REST 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 with SAP, SAP S/4HANA, SAP BTP, SAP Integration Suite/CPI, SAP MDG, or SAP-related data
• Experience applying AI or machine learning to manufacturing, product configuration, order management, forecasting, quality, supply chain, or operational processes
• Experience with MLOps 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 as OpenAI Codex, Claude Code, or GitHub Copilot
• Experience taking AI solutions from proof of concept through production 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