Overview: I’d position myself as the go-to SRE for AI reliability by building repeatable artifacts, creating visibility, mentoring talent, and tracking measurable adoption and hiring impact. Below is a 12‑month, quarter-by-quarter plan with concrete deliverables and metrics.
Months 0–3 (Foundations)
- Deliverables: baseline audit of AI infra and reliability gaps; publish an internal “AI Reliability Playbook” (SLOs for models, input/data validation, drift detection, rollout/rollback runbooks, Canary/Shadow strategies).
- Enablement: start weekly 1‑hour office hours for engineers and PMs; run a 90‑minute training on “Designing SLOs for ML systems.”
- External: outline 3 blog topics and conference submission ideas.
- Metrics: baseline SLO uptime, number of teams using playbook (target 2), office-hours attendance, blog outlines completed.
Months 4–6 (Scale and Enable)
- Deliverables: expand playbook into templates (alerting configs, monitoring dashboards, postmortem template for model incidents).
- Enablement: monthly hands‑on workshops (injecting data drift, simulation-driven chaos for model infra).
- Mentoring: launch a 6‑month mentorship cohort for 6 junior SRE/ML infra engineers with biweekly checkpoints.
- External: first blog post + open-source a small tool (drift-checker script / observability exporters) with tests and docs.
- Metrics: playbook adoption (target 50% of AI teams), workshop NPS ≥4/5, mentorship retention, OSS repo stars/forks.
Months 7–9 (Visibility & Thought Leadership)
- Deliverables: advanced playbook addenda (privacy-safe telemetry, cost-aware autoscaling), case study internal publication showing reduced incidents.
- External: submit and present a conference talk (postmortem + mitigations for a model outage); publish 2 technical blog posts (SLOs for models; chaos testing MLOps).
- Mentoring: sponsor mentees to present learnings internally.
- Metrics: reduction in model‑related incidents (target -30% vs baseline), number of external reads/shares (blogs 5k reads), conference acceptance, OSS contributors >3.
Months 10–12 (Institutionalize & Hire)
- Deliverables: packaged “AI Reliability Starter Kit” for new teams; incorporate materials into onboarding.
- Hiring/Recruiting: present at 2 external meetups/university talks; create hiring rubric for AI‑SRE candidates derived from playbook.
- Mentoring: convert top mentees into co‑instructors/maintainers for OSS/playbooks.
- Metrics: playbook usage across org (target 80% of AI teams), onboarding time reduction (target 20%), candidate pipeline increase from community activities (target 30% of hires attributed), interviews sourced from blog/OSS, retention of mentees, OSS contribution growth.
Ongoing: measure influence via monthly dashboards: playbook adoption %, incidents avoided, SLO compliance, attendance/NPS for training, OSS metrics (stars/PRs), content metrics (reads, shares), number of hire referrals from community. Use quarterly reviews to recalibrate topics and focus.
Why this works: It creates repeatable artifacts (playbooks/tools), builds credibility through public content and OSS, develops internal capacity via training/mentoring, and ties everything to measurable business outcomes—reliability improvements, faster onboarding, stronger hiring pipeline—so leadership can see ROI.
