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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Developer / Agentic AI Engineer - **Company:** NJTECH INC. - **Location:** Charlotte, NC, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Audit Trail, Microsoft Azure, Continuous Integration, DevOps, Information Extraction, Python (Programming Language), Key Management, Role-Based Access Control, Regression Testing, Software Engineering, Management of Software Versions, Google Cloud, Cloud Platform System, Data Ingestion, Large Language Models, Model Validation, Backend, Apache Kafka, Amazon Simple Queue Service (SQS), Servicenow - **Published:** August 14, 2026 - **Apply:** https://www.dice.com/job-detail/dd5d2d52-05d7-436b-85c9-c2abdde86630 ## About the Role * 4+ years of software engineering experience or equivalent with strong CS fundamentals * Hands-on experience building with LLMs and modern AI app stack (agents, RAG, tool/function calling). * Strong proficiency in Python and building back-end services/APIs. * Experience with at least one: LangChain / LangGraph, Llamalndex, Semantic Kernel or equivalent frameworks. * Experience with vector databases and search (e.g., Pinecone, Weaviate, Milvus, OpenSearch/Elastic, pgvector) * Experience deploying services in cloud environments (AWS/Azure/Google Cloud Platform) with basic DevOps practices * Strong understanding of security and privacy principles (PII handling, least privilege, audit logging), * Experience in financial services or other regulated domains (risk controls, compliance audit readiness) * Experience integrating with enterprise workflows (e.g., ServiceNow, Custom workflow engines, BPM/RPA) * Familiarity with model evaluation approaches (LLM-as-judge, rubric scoring, retrieval evals, offline/online testing) * Experience with messaging/eventing (Kafka/SQS), email ingestion pipelines, and document processing * Exposure to MRM concerns and governance (model cards, risk assessments, validation processes) ## Description * Build and enhance LLM/agent orchestration (Planner/supervisor patterns, tool-using agents, routing, guardrails). * Implement intent classification information extraction validation and decision logic for servicing workflows * Developed tool calling integrations to downstream systems (CRM, workflow engine, core banking services, case management) * Implement human-in-the-loop workflows (review, approval, escalation, override) based on confidence/risk thresholds Knowledge and grounding (RAG) * Design and implement retrieval-augmented generation (RAG) for policy procedure grounding and resolution guidance * Build knowledge ingestion pipelines with refresh/versioning * Improve answer quality via chunking strategies, embeddings re ranking and context management Quality, Safety and Evaluation * Define and run evaluation frameworks: golden datasets, scenario tests, regression tests, and automated scoring. * Reduce hallucinations and risk by implementing prompt policies, constraints, structured outputs, and verification steps. * Partner with risk slash compliance to ensure traceability, audit logs, explain ability requirements are met. Production Readiness and Operations * Implement observability for agents (latency, cost, tool failures, drift, quality signals, escalation rates). * Support CI/CD for agent prompts and configurations (versioning, approvals, rollback). * Collaborate with platform and security teams on secrets management, access controls, PII protections, and safe deployments. ## Related Videos - [Applying Agile Principles to Incident Management ](https://www.wearedevelopers.com/videos/101-applying-agile-principles-to-incident-management) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Java Meets AI: Empowering Spring Developers to Build Intelligent Apps](https://www.wearedevelopers.com/videos/1554-java-meets-ai-empowering-spring-developers-to-build-intelligent-apps) - [AI Killed DevOps... What Now? - Lee Faus](https://www.wearedevelopers.com/videos/1759-ai-killed-devops-what-now-lee-faus) - [AI in Production: applied AI & enterprise use cases](https://www.wearedevelopers.com/videos/100130-ai-in-production-applied-ai-enterprise-use-cases) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)