AI Application & Prompt Specialist

HARTING stands for strong connections – across the globe. As one of the leading international suppliers of industrial connectivity, we are connecting customers to their digital future. And as an employer? We connect around 6,000 people at our headquarter in Espelkamp and at locations worldwide. Here you’ll find great colleagues, as well as ever new opportunities and innovations revolving around IoT and artificial intelligence. In everything we do, we remain true to our roots: as a regionally connected family business that always stays firmly grounded in spite of our stellar high-tech. Here’s to your unique future with us: Yours!

Job Title: AI Application & Prompt Specialist Required Experience (3–7 Years) 3+ years building software systems with hands-on Python development (production code, packaging, testing) Practical experience integrating LLMs and building RAG pipelines; experience with LangChain and/or LangGraph or equivalent agent frameworks Experience with vector databases and embeddings workflows (Qdrant, OpenSearch, FAISS, Milvus or similar) Cloud deployment experience (AWS required or strongly preferred; experience with Bedrock or other cloud model-hosting services advantageous) Containerization (Docker), CI/CD pipelines, and basic infra-as-code or orchestration knowledge Familiarity with REST/async APIs, WebSockets, and relational DBs (PostgreSQL or equivalent) Demonstrable prompt engineering skills and experience measuring prompt performance and reducing hallucinations Strong engineering practices: modular code, unit tests, code reviews, documentation, and reproducible deployments Clear communication and cross-functional collaboration skills

Roles And Responsibilities Design, build and productionize internal AI solutions that combine strong prompting/LLM experience with production-grade Python engineering, agent orchestration, and secure enterprise integrations. Deliver reusable frameworks, observability, and guardrails so business teams can adopt LLM-enabled features safely and reliably.Build reusable SDKs, libraries and microservices for LLM integrations (prompt templates, prompt chaining, tool calling, caching, retry & backoff logic) Architect and implement multi-agent orchestration and workflow systems using LangGraph and/or LangChain patterns to support supervisor/worker and tool-calling agent designs Implement secure RAG pipelines: document ingestion, chunking strategies, embeddings, vector DB integration (Qdrant, OpenSearch, FAISS-style stores) and retrieval tuning Write production Python code (APIs, async messaging, WebSockets, background workers) to integrate LLMs (cloud-hosted or private) and vector stores; package services with containers and CI/CD Deploy and operate LLM components on cloud platforms (AWS + Bedrock or equivalent; familiarity with Azure OpenAI is a plus) and manage secrets/OAuth2 and RBAC integrations Define and run systematic prompt evaluation and monitoring (accuracy, hallucination, cost-per-response); implement guardrails and automated tests for prompt suites (unit tests, A/B experiments) Integrate AI features with enterprise systems (ServiceNow, SAP SuccessFactors, internal HR/ERP systems) to enable end-to-end workflows (ticket creation, approvals, data updates) Implement observability/alerting (latency, throughput, cost, drift, hallucination rates) and incident handling; produce runbooks and monitoring dashboards Collaborate with product, legal, security and domain experts to ensure privacy, compliance and acceptable use; implement technical controls (input redaction, auditing, access controls) Document patterns, run enablement sessions and deliver onboarding materials for internal developers and business users

Primary Skills Python (production-grade code, packaging, typing) LangChain and LangGraph (agent orchestration, prompt chaining, tool calling) RAG: ingestion, chunking strategies, embeddings, vector search and retrieval tuning Cloud: AWS (S3, Lambda, ECS/EKS, Bedrock or model-hosting services); familiarity with Azure OpenAI helpful Databases: PostgreSQL, session state and audit logging best practices DevOps: Docker, CI/CD, monitoring/observability tools, basic Kubernetes concepts Security: OAuth2, role-based access control, input filtering/redaction, audit trails

Prompting & Evaluation Capabilities Craft and iterate high-quality prompts and templates for retrieval, summarization, instruction following and tool use Build reusable prompt libraries and parameterized templates for scale Implement automated prompt testing and evaluation pipelines (unit tests, A/B experiments, quantitative metrics) Define and monitor metrics: accuracy, usefulness, hallucination rate, latency, and cost per useful response Design mitigation patterns for failure modes (refusal strategies, retrieval confidence thresholds, fallback flows) Governance, compliance & security Apply enterprise AI usage rules: use approved/private model instances, avoid uploading personal or strictly confidential data into external models, and enforce output review processes where required Implement technical enforcement: input filtering/redaction, access controls, logging/auditing and support approval workflows for publishing outputs Collaborate with information owners, legal and security teams to ensure policies are enforced and audits supported

Secondary Skills Hands-on experience with LangSmith or similar evaluation/orchestration tooling Experience with LLM fine-tuning or instruction-tuning workflows Experience building internal SDKs, admin panels or developer platforms for AI adoption Domain knowledge in industrial automation, manufacturing, connectivity or related fields

Interview & Assessment Suggestions Take-home task: build a small Python microservice that performs RAG using a vector DB, exposes a prompt template interface, includes unit tests and basic monitoring metrics Live exercise: iterate prompts for a defined internal workflow, demonstrate evaluation choices and explain failure modes and mitigations System design: outline architecture for safe LLM integration at enterprise scale (ingestion, retrieval, orchestration, monitoring, access control) Request GitHub or code samples, architecture diagrams, and runbook/monitoring artifacts where available

Professional Competencies

• Technical Expertise
• Analytical Skills
• Demonstrate Ownership
• Communication
• Leadership
• Execution
• Technological Insight
• Process Optimization Knowledge
• Business & Financial Acumen
• Trust and Collaborate

Culture Competencies

• Become better everyday
• Break new ground
• Champion customers satisfaction
• Demonstrate ownership
• Trust and collaborate Benefits: Staff insurance coverage (APAC)