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Generalist senior ML title and mid-level experience increase candidate competition.
Specialized ML/LLM and MLOps skills are moderately transferable but favor ML-focused backgrounds.
Explicit 5+ years requirement plus mandatory LLM, PyTorch/TensorFlow and MLOps skills raises filter strictness.
Design, train, and deploy end-to-end production-grade ML models and pipelines covering data ingestion to model serving.
Fine-tune and deploy large language models tailored for enterprise-scale use cases.
Mentor junior engineers and collaborate cross-functionally to develop AI-powered features and improve model performance.
5+ years of professional ML/AI engineering experience.
Strong proficiency in Python and experience with PyTorch or TensorFlow.
Experience with large language model (LLM) fine-tuning, retrieval augmented generation (RAG) architectures, or agent frameworks.
Familiarity with MLOps tools such as MLflow, Weights & Biases (W&B), or Kubeflow.
Candidate experienced in deploying AI/ML solutions in enterprise or production environments, with demonstrated end-to-end ownership.
Comfortable working asynchronously and collaborating in a remote-first or flexible workplace culture.
Practitioner with hands-on skills in deploying and optimizing large-scale language models and ML pipelines, plus mentoring teammates.