Verified Job
Engineering - Architecture
AI/ML Engineer (3-4 Years Experience)
Engineering - Architecture
#14825
Remote / WFH
Perimattic
Job Description
We are not looking for someone who knows how to call OpenAI's API and call it AI.
We are looking for an engineer who has rolled up their sleeves — trained models from scratch or fine-tuned foundation models on custom datasets, debugged loss curves, written custom training loops, and shipped something that runs in production.
What you will work on
At Perimattic, you will be building AI systems that go inside real enterprise products — document intelligence, manufacturing analytics, and agentic workflows. You will work on model selection, fine-tuning, evaluation pipelines, and inference optimization. You will own the full lifecycle from problem framing to deployment.
What we expect from you
3 to 4 years of hands-on experience in ML engineering, not AI product management
You have trained or fine-tuned models — transformers, CNNs, or similar — on real datasets with real constraints
Solid command of PyTorch or TensorFlow, not just the scikit-learn docs
You understand what overfitting actually looks like in a training run, not just in theory
Experience with model evaluation, dataset curation, and iterative experimentation
Comfort working with unstructured data - documents, images, or time-series
Bonus if you have worked with LLM fine-tuning (LoRA, QLoRA, PEFT), RAG architectures, or ONNX/TensorRT for inference
Bonus if you have deployed models to production not just notebooks
We are looking for an engineer who has rolled up their sleeves — trained models from scratch or fine-tuned foundation models on custom datasets, debugged loss curves, written custom training loops, and shipped something that runs in production.
What you will work on
At Perimattic, you will be building AI systems that go inside real enterprise products — document intelligence, manufacturing analytics, and agentic workflows. You will work on model selection, fine-tuning, evaluation pipelines, and inference optimization. You will own the full lifecycle from problem framing to deployment.
What we expect from you
3 to 4 years of hands-on experience in ML engineering, not AI product management
You have trained or fine-tuned models — transformers, CNNs, or similar — on real datasets with real constraints
Solid command of PyTorch or TensorFlow, not just the scikit-learn docs
You understand what overfitting actually looks like in a training run, not just in theory
Experience with model evaluation, dataset curation, and iterative experimentation
Comfort working with unstructured data - documents, images, or time-series
Bonus if you have worked with LLM fine-tuning (LoRA, QLoRA, PEFT), RAG architectures, or ONNX/TensorRT for inference
Bonus if you have deployed models to production not just notebooks
