AI Application & Prompt Specialist
Software Engineering, Data Science
Chennai, Tamil Nadu, India
Posted on Aug 28, 2026
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
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
- Become better everyday
- Break new ground
- Champion customers satisfaction
- Demonstrate ownership
- Trust and collaborate