Application Engineer

**You'll join the AI Products squad to build the next generation of LLM\-powered applications for the staffing industry. This is a product engineering role, not a research role. You'll ship real AI products used by real recruiters and real clients.** We're not looking for someone who trains models from scratch. We're looking for someone who knows how to build with LLMs — orchestrating agents, designing RAG pipelines, crafting prompt architectures, and shipping applications that solve real business problems. If you've built a chatbot, an agent, or a RAG app that actually went to production (or close to it), we want to talk. **What You'll Build** Example products in the pipeline · AI Candidate Matching Engine — Match candidates to job openings using semantic search, skill extraction, and intelligent ranking. · Intelligent Onboarding Agent — An agentic system that guides new hires through credentialing, document collection, compliance checks, and first\-day preparation. Replaces manual follow\-up chains. · Recruiter Copilot — An AI assistant embedded in recruiters' daily workflow. Surfaces candidate suggestions, drafts outreach, summarizes conversations, and flags placement risks. · Conversational BI — Natural language interface to business data. Users ask questions in plain English, get charts, tables, and insights back. What You'll Do · Design and build agentic systems — multi\-step AI workflows that reason, retrieve, and act across multiple tools and data sources · Build RAG (Retrieval\-Augmented Generation) pipelines — connect LLMs to company data through vector databases, semantic search, and context injection · Craft prompt architectures — design system prompts, few\-shot examples, chain\-of\-thought patterns, and guardrails for production LLM applications · Integrate with APIs and data sources — consume data from our CDI platform (Microsoft Fabric Lakehouse), ATS (JobDiva), VMS platforms, and internal systems · Ship production\-grade applications — not prototypes. Build with error handling, monitoring, logging, fallback strategies, and user\-facing quality · Collaborate with the Data Platform squad — define what data you need in what format, and work with data engineers to make AI\-ready datasets available · Evaluate and iterate on AI quality — build evaluation frameworks, test edge cases, measure accuracy, and improve outputs systematically · Stay current with the LLM ecosystem — new models, frameworks, and techniques ship weekly. You'll help evaluate what's worth adopting. What We're Looking For Must have (1\-3 years of experience): · Shipped at least one LLM\-powered application in a real organization (not just personal projects or tutorials). This could be a chatbot, agent, RAG app, content generator, or similar. · Strong Python skills — you think in Python, not just write it · Hands\-on experience with LLM orchestration frameworks — LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, or similar · Experience building RAG systems — vector databases (Pinecone, Weaviate, Chroma, FAISS), embedding models, retrieval strategies, context window management · Understanding of prompt engineering at a production level — not just getting good outputs, but building reliable, consistent, testable prompt architectures · Familiarity with LLM APIs — OpenAI, Anthropic, Azure OpenAI, or open\-source model serving (Ollama, vLLM) · Basic understanding of software engineering practices — Git, API design, error handling, logging · Good communication in English (daily collaboration with a distributed team) Nice to have: · Experience with agent frameworks — multi\-agent orchestration, tool use, function calling, planning loops · Familiarity with fine\-tuning LLMs (LoRA, QLoRA, instruction tuning) — not required but valuable for future work · Experience with evaluation frameworks for LLM outputs (RAGAS, custom metrics, human\-in\-the\-loop evaluation) · Knowledge of NLP fundamentals — text classification, NER, semantic similarity, embeddings · Exposure to cloud deployment — Azure, AWS, or GCP for hosting AI services · Familiarity with staffing, HR tech, or recruiting industry — understanding placement lifecycles, compliance, credentialing · Experience with Microsoft Fabric, Power BI, or Azure ecosystem **Who You Are** · You graduated recently (1\-3 years ago) and have already gotten your hands dirty building real AI applications in a real organization — internship, first job, startup, or contract work · You're more of a builder than a researcher. You care about shipping products, not publishing papers. · You're excited by the pace of LLM innovation and naturally stay up to date with new models, frameworks, and techniques · You're comfortable with ambiguity. Some of these products don't have a playbook — you'll figure it out with the team. · You're autonomous but collaborative. You can own a product end\-to\-end but also work closely with data engineers and product stakeholders. Why This Role · Build AI products at scale from day one. Our data infrastructure covers millions of candidates, Fortune 500 clients. The problems are real and the impact is immediate. · Full product ownership. You won't be writing prompt templates in a vacuum. You'll own products end\-to\-end: architecture, data requirements, prompt design, API integration, quality, and iteration. · Cutting\-edge stack. LLMs, agents, RAG, vector databases, agentic workflows — all in production, not demos. · Growth trajectory. As the team scales toward an AI Products Squad (2\-3 engineers), early joiners will shape the team culture and grow into lead roles.