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Protocol Intelligence
Data-driven signals on your job's competitivenessSpecialized ML/GenAI skills plus enterprise deployment reduce but still attract capable applicants.
Core ML and GenAI skills transfer broadly, but enterprise insurance and governance context raise domain specificity.
Many mandatory technical, production and governance requirements imply strict filtering of candidates.
Job Description
Structured overview of role & requirementsAbout This Role
Lead end-to-end development and operationalization of machine learning models including problem framing, experiment design, evaluation, and production monitoring.
Design and deploy AI/ML solutions using NLP, Generative AI, and cloud AI platforms while ensuring compliance with enterprise AI governance standards.
Communicate complex modeling decisions, evaluation results, and risks to both technical and non-technical stakeholders to drive measurable business impact.
Minimum Requirements
3+ years of experience applying deep learning architectures in real-world use cases.
Proficiency in Python, including pandas, NumPy, scikit-learn, and strong SQL skills; familiarity with PyTorch and/or TensorFlow preferred.
Experience with cloud-based AI platforms (Google Vertex AI, AWS SageMaker, or Azure AI Services) and containerization supporting deployment workflows.
Work Experience Required: Minimum 3 years in relevant machine learning and AI roles.
Ideal Candidate Profile
Deep expertise in machine learning lifecycle management within complex enterprise or packaged application environments.
Proven ability working with unstructured data, including document parsing, OCR, NLP, and Generative AI techniques such as embeddings, prompt engineering, and RAG solutions.
Experienced in implementing AI governance, ethical standards, privacy, and compliance in model development and deployment.