Problem
Search, funding, and role decisions scatter across sources and become hard to compare or revisit.
Automated daily pipeline: funding signals → job discovery → fit scoring → report outputs.
Project information
- Category: Automation · LLM Apps · Pipelines
- Flow: Funding signals → company enrichment → ATS/careers discovery → role relevance → fit scoring → report outputs
- Ops: Local cron + AWS serverless deployment option
- Safety: Human-in-the-loop drafts (never auto-apply)
Overview
Personal Career Ops is a blueprint for automating a daily job-search workflow with a focus on signal quality, structured scoring, and repeatable outputs. It turns fresh funding events into a ranked list of roles with fit explanations and draft materials.
The design emphasizes guardrails (no auto-applying) and practical deployment: run locally on a schedule, or deploy as a serverless daily job with durable outputs.
Source code: private / available on request.
Mechanism
Automated collection, structured fit reports, and personal workflow pipelines turn loose inputs into ranked decisions.
What It Proves
Small internal tools can make ambiguous decisions inspectable without pretending every signal is equally strong.
Engagement Relevance
Shows systems taste and automation discipline; it is supporting proof, not a client engagement surface.
CTA
Use this as supporting evidence while reviewing scoped reliability work.
Request a fit callWhat It Demonstrates
- Pipeline design for daily batch automation with persistent state and outputs.
- Practical web automation patterns: company enrichment, ATS detection, and listing normalization.
- Explainable ranking via fit scoring + rationale suitable for human review.
- Production posture: API key protection, rate limiting, and AWS deployment considerations.