Founding AI Engineer

About this role As Founding AI Engineer at Ajentik, you'll own the intelligence layer of Elderwise end-to-end. Fine-tuned models, embeddings, retrieval, and evaluation systems that turn what a caregiver actually said into structured, EMR-ready clinical documentation a clinician can trust. You'll own the AI architecture from the ground up and, as we grow, build the team behind it. This is a hands-on, high-ownership seat for someone who wants their code measured against a clinical bar and is energised by that. What you'll do Own the model layer end-to-end. Fine-tune and adapt LLMs to map free-text caregiver notes onto validated instruments with structured output a clinician doesn't have to second-guess. Build the embeddings and retrieval layer. Chunking, hybrid search, reranking, and the retrieval plumbing that decides whether the system is genuinely useful. Grow domain embeddings that become a moat. Make evaluation gate every change. Golden datasets, LLM-as-judge, regression tests, and human-in-the-loop review, with accuracy, safety, cost, and latency all treated as first-class metrics, not things we circle back to. Engineer for our offline-first, multi-provider reality. Cloud today, with a hard bias toward local/edge inference (quantisation, on-device), because in eldercare the network isn't a given. Extend our conversational AI. The question-generation and voice pipeline, so an interview feels like a conversation rather than a form read aloud. Own production health. Tracing, prompt versioning, drift detection, privacy-safe logging, and the discipline of learning something's broken from your dashboards rather than from a clinician. Turn clinical validity into systems. Partner with our Clinical Lead to translate "this is how a good geriatric assessment works" into specifications, eval sets, and guardrails. Push the frontier and keep us current. Track new open models, fine-tuning methods, and retrieval techniques; run fast experiments; ship the ones that survive contact with the eval harness. Requirements Ship first, then measure. You'd rather have a rough thing in production this week that you can improve than an elegant thing in a design doc next quarter. Resourceful under ambiguity. Handed an unfamiliar tool, framework, or clinical instrument on Monday, you have something working by Wednesday and an informed opinion about it by Friday. Genuinely trying to get better. You read other people's code, papers, and post-mortems, and treat being wrong as cheap information rather than a bad day. Hands-on experience taking LLM-powered systems from prototype to production, where accuracy and reliability were the whole point. Demonstrated fine-tuning and embeddings/retrieval work shipped and operated for real (LoRA/QLoRA, RAG, vector search) Strong Python and comfort with a production backend framework (FastAPI, Flask, or similar). A track record of designing evaluation systems for LLM applications Fluency with LLM safety in practice: guardrails, adversarial testing, and a healthy respect for failure modes that surface at the worst possible time. A pragmatic read on regulated environments, sensitive patient data, PDPA-style governance. Nice to have Healthcare, clinical informatics, or another domain where a mistake is expensive. Health data standards (FHIR R4, HL7) or medical ontologies (SNOMED CT, LOINC, ICD-10). Self-hosted / edge inference (vLLM, llama.cpp, GGUF, quantisation, on-device mobile ML). Voice/conversational AI pipelines. Spec-driven, agentic development style. How to apply Send your CV and a short note — GitHub, papers, or a system you're proud of, to board@ajentik.ai. Tell us about something you fine-tuned or evaluated, what broke, and what you did about it. Ajentik AI is an equal-opportunity employer. We welcome applicants of all backgrounds.