Applied AI Engineer - Healthcare Domain

The Role :Work at the forefront of medical AI - not by wiring together off-the-shelf services, but by owning the systems and the models behind them. Clinical work is messy, spoken, time-critical, and unforgiving of errors, which makes it one of the most interesting applied AI problem spaces there is. The headline opportunity: pushing the frontier on special-purpose models of our own - fine-tuned, highthroughput, domain-adapted models that beat general-purpose LLMs on the narrow things medicine actually needs, at a fraction of the cost and latency. Prior fine-tuning experience is not required; we want strong applied AI engineers with solid fundamentals and the motivation to grow into this, and we'll support you in doing so.What you'll do:- Real-time clinical document generation - turn a live consultation into a structured, clinically accurate document while the clinician is still in the room. Sub-second latency, zero tolerance for hallucination.- Fine-tuning special-purpose models - LoRA, PEFT, SFT and preference alignment, optimized for highthroughput serving. Dataset design through to production. This is where we invest in going deep.- Speech-to-text and audio pipelines - real clinical audio: multiple speakers, accents, noise, and Swedish and German medical vocabulary no off-the-shelf model has seen.- Medical NER and structured extraction - medications, dosages, diagnoses, procedures and temporal relationships pulled out of free text, reliable enough to write into a patient record.- Evaluation pipelines - in healthcare, "it looks good" is not a metric. Golden datasets, automated judges, clinician-in-the-loop review, and the monitoring that catches regressions before a clinic does.- Automated booking and scheduling - agentic systems that handle real patient interactions end to end, with real-world consequences when they go wrong.- Data and deployment - curate clinical datasets to a training-ready standard, and take systems from experiment to monitored, production-grade services clinicians rely on at scale. You won't do all of this at once, but over time you'll touch most of it.What we're looking for :- Bachelor's in Computer Science, Engineering, or a related field.- 3+ years in AI/ML, with a focus on applied and Generative AI systems.- Strong hands-on experience with LLMs, prompt engineering, and structured outputs.- Hands-on with speech-to-text and audio-processing pipelines.- Proficient in Python (pandas, NumPy, ML/AI libraries) and SQL.- Familiar with cloud AI services, preferably AWS (S3, SageMaker, Bedrock).- Understanding of LLMOps / MLOps: evaluation, monitoring, deployment, and iteration.- Proven experience shipping production-grade AI systems at scale.- Strong ownership, clear communication, and independence in a fast-paced environment.Good to have :- Parameter-efficient fine-tuning (LoRA, PEFT, SFT) and tools such as Hugging Face Transformers, PEFT, TRL, Axolotl, or Unsloth.- RLHF, DPO, or other preference-alignment methods.- Information extraction / NER, and experience with clinical or biomedical ontologies.- End-to-end GenAI workflows (LangChain, LlamaIndex, RAG), embeddings, vector databases, and retrieval.- Agentic systems, tool use, and function calling in production.- Model quantization, distillation, or inference optimization; distributed / multi-GPU training.- Real-time / streaming inference and low-latency serving.- Healthcare, clinical, or other regulated-data experience. (ref:hirist.tech)