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Remote role and mid-level seniority increase competition; specialized ML/LLM skills limit applicant pool.
Core ML, MLOps and LLM skills transfer across industries, but identity-security domain knowledge increases specialization.
Explicit 8+ years, specialized ML/LLM, MLOps, and domain expertise create strict screening filters.
Lead the AI stack development lifecycle from opportunity sizing and prototyping to deployment and impact measurement.
Own performance, validation, and inference of real-time machine learning models including LLM-based systems.
Establish model development standards, governance, security, and cost management while mentoring analysts and coordinating cross-functional stakeholders.
8+ years professional experience in analytics, data science, machine learning including applied ML and LLM development.
Proficiency in Python, SQL, ML frameworks, and MLOps including feature engineering, deployment, and monitoring.
Experience building real-time anomaly detection and large-scale data processing systems.
Not explicitly mentioned: specific degree requirements, notice period, or strict location constraints (role is remote).
Demonstrated experience in business-facing ML solutions such as churn prediction, revenue forecasting, and marketing mix optimization.
Domain knowledge in identity threat detection and behavioral anomaly analysis.
Comfortable working in fast-paced, data-driven environments or startups where ML drives business outcomes and can communicate complex technical topics clearly to senior stakeholders.