Employer-facing intent. Evaluates a candidate's profile against a job description and returns a structured prescreening report: overall scores, a proceed/decline recommendation, cited strengths and concerns, risk flags, and recommended interview focus areas.
{ "success": true, "data": { "output": { "overall_score": 88, "fit_score": 85, "skill_score": 92, "experience_score": 90, "confidence": { "score": 0.93, "level": "high" }, "recommendation": "strong_proceed", "recommendation_rationale": "Candidate exceeds core technical requirements with 9 years of relevant experience and direct PyTorch and AWS skills. Minor gap in MLOps tooling is addressable.", "strengths": [ { "strength": "Deep PyTorch expertise", "evidence": "Led model training infrastructure at previous employer", "relevance_to_role": "Directly maps to core technical requirement" } ], "concerns": [ { "concern": "No explicit MLOps tooling mentioned", "severity": "minor", "evidence": "Resume lists model training but not deployment pipelines", "suggested_probe": "Walk me through how you've handled model deployment and monitoring in production." } ], "risk_flags": [], "interview_recommendations": { "recommended_interview_type": "Technical + system design", "focus_areas": ["MLOps pipeline ownership", "Scale of previous deployments"], "questions_to_ask": [ "Describe your largest production ML system end-to-end.", "How have you handled model drift in production?" ] }, "executive_summary": "Strong candidate with 9 years of directly relevant experience. Skill match is excellent. Recommend fast-tracking to technical interview." } }}