Microsoft Core AI VP Interview

EXAMPLE: Single Ads AI Agent Stack Situation: My team built first agent runtime, scaled to 60M+ sessions/year. Frontend Ads built their own stack—political/org reasons, wanted independence from consumer. Challenge: Multi-year unification battle. Two stacks: "Service Agent" vs "Product Agent." Resolution: SVP eventually directed single stack. Worked with senior director through productivity council (all LA+ engineering leads). Key decisions: Stack architecture, when to unify vs wait, organizational alignment. Lesson: Org design and relationships matter as much as technical architecture. --- ALTERNATE EXAMPLE --- Email Automation Evolution: • Elixir: Direct F1 calls, 2 CUJs • OSA Studio: Democratized plan building (org challenge) • Catalyst Autoplan: Routing challenge • Catalyst: Current unified approach Technical bet that didn't work: Routing complexity in OSA. Lesson: Organizational readiness matters as much as technical capability. 📌 Question to Ask: "As a Distinguished Engineer leading a team, how do you stay close to the code? What's your workflow for balancing leadership with technical depth?"

DESIGN: LLM INFERENCE SERVING AT SCALE ═══ REQUEST FLOW ═══ Client → API Gateway → Auth → Rate Limiter → Router → Serving Pool → Response ═══ 1. API GATEWAY & AUTH ═══ • TLS termination, request validation • Auth: API keys, OAuth tokens, or managed identity • Extract: model_id, max_tokens, temperature, user_id • Quota lookup: which tier? (free/pro/enterprise) ═══ 2. THROTTLING & RATE LIMITING ═══ • Token bucket per user/org (e.g., 100K tokens/min) • Separate limits: requests/min AND tokens/min • Priority queues: paid > free, short > long • Backpressure: 429 with Retry-After header • Circuit breaker: if pool unhealthy, fail fast ═══ 3. ROUTING ═══ Model Router decides WHERE to send: • Model affinity: GPT-4 → Pool A, Claude → Pool B • Latency SLO: p50 < 200ms? Route to fastest pool • Cost optimization: batch small requests together • Geographic: route to nearest region Context-Aware Routing: • Prefix caching: same system prompt? Route to GPU with cached KV • Session affinity: multi-turn → same server (KV cache reuse) • Load balancing: least-connections or weighted round-robin ═══ 4. POOLS & POD MANAGEMENT ═══ • Pool = group of GPU nodes serving same model • Pod = K8s unit (1-8 GPUs with tensor parallelism) • Autoscaling: scale on queue depth, not CPU - Scale-up trigger: queue > 100 requests for 30s - Scale-down: idle GPUs for 5 min (expensive!) • Warm pools: keep N pods hot for burst traffic • Preemption: spot/preemptible for batch, on-demand for realtime ═══ 5. CONTINUOUS BATCHING (ELI5) ═══ OLD WAY (Static Batching): Imagine a bus that waits for 8 passengers, drives to destination, then comes back. If passenger 1 wants to go 1 mile and passenger 8 wants 10 miles, everyone waits for the 10-mile trip. NEW WAY (Continuous Batching): Imagine a bus that picks up/drops off passengers at EVERY stop. Passenger 1 gets off at mile 1, and a NEW passenger gets on immediately. The bus is always full and always moving. TECHNICAL: • Iteration = 1 decode step (generate 1 token per sequence) • After each iteration, check: any sequence done? • If done → evict from batch, slot open • If slot open → insert waiting request immediately • Result: no head-of-line blocking, GPU always saturated 📌 Question: "What's your framework for deciding when to build a new service versus extending an existing one?"

EXAMPLE 1: EMR (Engineering Metrics Review) Manager wanted to replicate sales-style metrics review. Support team stalled 6 months claiming difficulties. I led by example—built dashboard from what we DID know rather than waiting for perfect. Started the process, fixed it monthly. Now a cultural norm. Team owns and improves it without me. Lesson: Start imperfect, iterate openly, empower others. EXAMPLE 2: Support Lead Post-Layoff Led team through org change after layoffs. Prioritized emotional support first—acknowledged loss, created space to process. Maintained open dialogue. Built Marketing Advisor team during this period—crisis created opportunity to rebuild culture intentionally. EXAMPLE 3: Swim Lane Tracker Introduced to show work distribution across teams. Made hidden work visible, surfaced load imbalances. Data revealed patterns for rebalancing. Teams saw fairness because it was transparent. Lesson: Build systems that make the right thing visible. PHILOSOPHY: Growth mindset in action, not slogans. Crisis can accelerate culture change when you lead with empathy first, then systems. 📌 Question to Ask: "You spent 16 years at Amazon building teams. What surprised you most about Microsoft's culture when you joined, and what do you think Microsoft does better?"