09 — study Conversion¶

How 13 months of Viking discipline converts into a job offer. Month 10 is when the campaign starts.

The frame¶

The portfolio you build across 01_foundations/ through 12_portfolio/ is your CV. Not a supplement to it. Not a companion piece. The CV itself. When a hiring manager at Fireworks or Baseten looks at your application, the first thing they will click is your GitHub. The second thing is your blog. The resume PDF is a formality — filled by the artifacts.

That’s the good news: you don’t have to be clever about “positioning.” The work is the position.

The less-good news: you have to actually run the applications process, and it’s a job in itself. The plan below turns that job into 10-12 hours a week from month 10 onward, running in parallel with the last three sprints of technical work.

The timeline¶

Months 1-9    Build. Ship. Publish. No application activity.
Month 10      Portfolio landing page. Warm outreach begins.
Month 11      Formal applications. First study loops.
Month 12      studies continue. Negotiate.
Month 13      Decide. Sign.

Yes, month 10, not month 12. study loops at good inference orgs take 4-8 weeks. If you wait until month 12, you’ll accept an offer in month 15 and the roadmap will have run over. Start early, run in parallel with your last technical sprints.

The target org taxonomy¶

Apply deliberately. Blanket-applying is for people without portfolios. You have one. Use it as a scalpel.

Tier 1 — Pure inference orgs (best fit for you)¶

vLLM ecosystem: Neural Magic (now Red Hat), Anyscale
LMSYS orbit: any org spun out of LMSYS
Inference-as-a-service: Fireworks AI, Together AI, Baseten, Replicate, Modal, Runpod, DeepInfra, OctoAI (post-NVIDIA)
Model-and-inference: Groq, Cerebras (harder — they want their own stack), SambaNova

Why tier 1: the work is 100% what you trained for. Compensation is strong (₹40-90 LPA equivalent for remote roles, sometimes higher for staff-level). Remote-friendly by default. Zero visa nightmare. Your merged vLLM PR is a golden ticket into most of these.

Tier 2 — Hyperscaler LLM infra teams¶

AWS: Bedrock inference, SageMaker LLM serving, Trainium/Inferentia teams
GCP: Vertex AI LLM serving, TPU serving stack
Azure: Azure OpenAI Service inference, ND-series infra
Meta: FAIR infra, Llama serving infra (rarer to hire externally in India, but happens)

Why tier 2: massive scale, real problems, comp is excellent. Downside: bureaucracy, less choice in what you work on. Great if you want to see 100K+ QPS problems.

Tier 3 — Hardware + DevRel¶

NVIDIA: DevTech, Solutions Architect, DevRel for LLM inference in India
AMD: ROCm ecosystem, MI300 inference (growing team, undervalued opportunity)
Intel: Gaudi inference (long shot but they have India presence)
Cerebras/Groq: if you like their exotic hardware angle

Why tier 3: high leverage, get paid to talk to every important inference team on the planet, career-defining relationships. Downside: less pure engineering, more communication.

Tier 4 — Research lab engineering arms¶

DeepSeek (China-based but hires remote / Southeast Asia)
Mistral (Paris, remote-friendly for the right person)
Cohere (Toronto, some remote)
Meta FAIR infra
Anthropic / OpenAI research eng (very hard, but the vLLM PR opens the door if it’s good)

Why tier 4: working with frontier models, brilliant colleagues. Downside: highly competitive, often on-site or specific timezones, some visa complexity depending on org.

Tier 5 — Enterprise on-prem inference¶

Databricks (Mosaic AI serving)
Snowflake (Cortex)
NVIDIA Enterprise solutions
India-domestic: Fractal Analytics, Tredence, CRED (as they mature ML infra), Ola Krutrim, Sarvam AI, Zoho itself pivoting to inference (you know the landscape best here)

Why tier 5: matches your services / on-prem / enterprise background exactly. Some of these will value “knows how to actually deploy this in a real enterprise” more than the pure vLLM PR crowd. Different sales pitch, same portfolio.

The India-specific EV analysis¶

Bluntly, ranked by expected value for you:

Remote at tier 1 (Fireworks/Together/Baseten/Modal/Neural Magic). Compensation arbitrage in your favor — US comp at India cost of living. No visa headache. Time-zone: 4-6 hours of overlap needed, doable. Best combination of learning + comp + optionality.
Tier 2 India office (AWS Bengaluru, GCP Bengaluru, Azure Hyderabad). Steady, well-paid, credential-building. Less learning velocity than tier 1 but very stable.
Tier 3 NVIDIA/AMD India. Networking gold. Underrated. NVIDIA India is growing fast in 2026.
Tier 5 India-domestic (Sarvam, Krutrim, CRED, Zoho pivot). Interesting on mission, sometimes weaker on comp, occasionally weaker on technical bar. Case-by-case.
Move-to-US or move-to-EU roles. Highest ceiling but the visa/relocation cost is real. If you have specific reasons to want the move, worth it. Otherwise — the remote-tier-1 route dominates on paper.

Read this line twice: the remote-tier-1 route is the highest-EV path for an India-based engineer with a real inference portfolio in 2026-2027. It didn’t exist as an option 5 years ago. It’s the whole reason “become an inference engineer” is the right bet for you specifically.

study loop patterns¶

Different org types weight things differently. Know what you’re walking into.

The universal five stages¶

Most inference orgs run some subset of:

Recruiter screen (30 min). Behavioral, ballpark comp, why-us. Easy.
Hiring manager screen (45 min). Technical depth study scoped to their stack. Your portfolio does 80% of the work here.
Coding screen (60 min). LeetCode-medium. Not the differentiator, but you have to not fail it.
System design (60 min). “Design LLM serving for 10K QPS.” This is where the roadmap wins.
Deep-dive / kernel screen (60-90 min). Discuss a project of yours OR walk through a CUDA/Triton problem. Your SGEMM ladder + Triton FA2 work is your weapon here.

Most loops are one of each. Some tier-1s (Fireworks, Anyscale) skip coding entirely if your GitHub is strong. Some add an ML depth study.

The four things to prep, ranked by ROI for you¶

1. System design — “Design LLM serving for 10K QPS” (HIGHEST ROI)

You will ace this if you actually built the mini-engine in Phase 5 and understand production considerations from Phase 8. The prompt looks scary. It’s not scary for you. Key components to cover:

Request lifecycle: HTTP → queue → prefill → decode → streaming response
Batching strategy: continuous batching, chunked prefill, prefix caching
KV cache mgmt: PagedAttention, offloading, sharing
Sharding: tensor parallel, pipeline parallel, disaggregated prefill/decode (DistServe)
Autoscaling: what triggers scale-up (queue depth, TTFT p99), warm pool sizing
Observability: what to log, what to alert on
Failure modes: OOMs, stragglers, hot-key models

Rehearse this exact prompt 5-8 times before studies. Use a whiteboard or excalidraw. Time yourself to 45 minutes. Refine until it’s fluid.

2. Deep-dive on your own work

They’ll ask you to walk through the Triton FA2 project or the mini-engine. Anticipate: “Why this design? What did you try first? What surprised you? What would you do differently?” Your /lab_notes/ and retros are gold here — you can quote your own gap explanations verbatim.

3. CUDA/kernel screen

The SGEMM ladder repo (Phase 3 exit) IS the answer. Study it cold. Be able to talk about occupancy, tensor-core loading, shared-memory bank conflicts, register pressure, at conversational speed. If your Triton FA2 project shipped and you can explain the tile sizing tradeoffs — you’re above the bar for 90% of inference-org kernel screens.

4. LeetCode-medium coding

Don’t over-index. 40-60 mediums over month 10-11 is enough. Focus on:

Arrays / hashmaps (~40% of screens)
Trees / graphs (~30%)
DP light (~15%)
One-off (~15%)

Blind 75 or NeetCode 150 covers it. This is the least important prep dimension for inference roles. Don’t let LeetCode grind eat the sprint time that should be going into your last technical deliverable. 45 min a day, month 10-11. That’s it.

5. ML depth (varies)

Some loops (Cohere, Anthropic) will ask ML-arithmetic questions: “Estimate FLOPs for one forward pass of Llama-3-8B at seq=2048.” Your Phase 1 arithmetic notebook is directly the answer. Practice the classic estimates cold: FLOPs, memory, KV cache size, decode tok/s prediction. If you did Phase 1 properly, this study is a gift.

The portfolio landing page¶

One static page. Your domain. Owned distribution. Structure that works:

[Name]
inference engineer · tamil nadu · [email] · [github] · [x] · [blog]

> One-line pitch: "I build and benchmark LLM inference systems. Merged vLLM
>  contributor. Focused on on-prem enterprise serving."

== FEATURED WORK ==

1. [Merged vLLM PR #NNNN — title]
   what it does · what you learned · link to PR + blog writeup

2. [Mini inference engine — 800 lines, continuous batching]
   github link · benchmark link · blog writeup

3. [Triton FlashAttention-2 within 2x of official]
   github link · blog writeup · benchmark reproducibility

4. [Enterprise on-prem reference architecture]
   github link (infra-as-code) · blog writeup · goodput numbers

5. [AWQ vs GPTQ vs SmoothQuant — quality-latency curves]
   r/LocalLLaMA post · blog writeup · raw JSON results

== WRITING ==
chronological list of blog posts with 1-line each

== EXPERIENCE ==
Zoho — Applied ML Engineer — [dates] — 3-4 lines
(recs, prediction, agentic harnesses, on-prem + cloud)

== EDUCATION ==
keep brief. real work above dominates.

== CONTACT ==
email + LinkedIn + calendar link for 30-min chats

Rules:

Featured work is 3-5 items MAX. If you list 12, hiring managers click zero.
Each item has: 1-line description, 1 link to the code, 1 link to the writeup. Nothing else.
No skills list. Your skills are visible in the code.
No stock photos. No hero images. Fast-loading, plain, opinionated. This is a portfolio, not a bootcamp graduation page.
Mobile-responsive. Half your hiring managers will open it on a phone at 11pm.

Deploy on Cloudflare Pages, Vercel, or GitHub Pages. Cost: $0. Time: one sprint (or half a sprint if you use a template).

The cold email template¶

Use for warm-ish outreach in month 10-11, before applying formally. Target: an engineer at the org (not a recruiter), whose recent work you actually read.

Subject: your work on [specific thing] + a question

Hi [name],

I've been reading your PRs on [specific thing — e.g. "vLLM's prefix caching
 rewrite"] over the last few months and got a lot out of it. In particular
 [one specific thing you learned from their work, not generic praise].

Quick context: I'm an applied ML engineer at Zoho (India) who's spent the
last year going deep on inference — [link to landing page]. Recently shipped
[most impressive deliverable: e.g. "a Triton FA2 that hits 61% of peak on a
3090"] and my first vLLM PR is under review [link].

Two questions if you have 15 minutes:
1. [Specific technical question you actually want the answer to]
2. [Softer: is [org] hiring inference engineers in a remote/India setup right
   now, and if so, would you introduce me to the right person?]

Either way, thanks for the work — it's shaped how I think about this stuff.

— Raghul
[link]

Rules:

Specific, not generic. You must have actually read their work. Fakery is transparent.
Two questions max. Their time matters.
Value first. You’re offering context about yourself + a real technical curiosity, not just asking for a favor.
No attachments. One link.
Send Tuesday/Wednesday morning their timezone. 10-15% reply rate is normal for good cold emails. Send 20 over month 10, get 2-3 real conversations, one of them turns into an study lane.

Do NOT mass-send this. Ten thoughtful sends beat 200 sloppy ones. Every time.

Negotiating the offer¶

Brief version, but the money-on-the-table is huge if you get this right.

Never accept the first offer on the call. “Thank you — I need 48 hours to think it through with my family.” Every time.
Have a competing conversation, ideally an offer, in parallel. This is the single largest lever. Even a soft “we’re moving to final round” from another org changes the number 15-25%.
Ask for the full comp breakdown in writing (base, bonus target, equity type + vest schedule + strike, sign-on, relocation, remote allowance). Don’t compare on base alone.
Total comp anchors to level, not to your ask. Ask what the compensation range is for the level. Then ask to be considered at the top of the range given your portfolio.
Equity in private companies is a lottery ticket. Value it at 20-40% of paper-face-value for planning. Not zero, not full. Ask about liquidity events, refresh cadence, and vest cliff.
Get the offer in writing before resigning. Once signed by both parties, then — and only then — resign from Zoho.

This alone can be worth 20-40 lakh over a 3-year window. Take the 48 hours. Do the homework. It’s your salary.

The final rule¶

When the offers come — and if you do the work, they will — you’re going to have a choice. There’s the offer with the biggest number. And there’s the offer where you’ll learn the fastest.

Take the one that lets you keep learning fastest, not the one with the biggest number.

Here’s why. You are 26-ish, doing this in 2026-2027. You have roughly 40 more years of professional compounding ahead of you. The delta between offer A at 60 LPA and offer B at 75 LPA in month 13 is real — but it’s rounding error against “which team accelerates me by 2x for the next 3 years.” A 2x accelerator over 3 years makes you unrecognizable. A 25% comp bump does not.

Ask of each offer:

Who are the three people I’d learn most from on this team?
What’s the actual work — shipping features, or writing kernels?
Is the manager technical? Would I trust them in a debugging session?
Does the roadmap have things I don’t know how to do yet? (Yes = growth. No = plateau.)
Can I take a 4-week sabbatical in year 2 without political cost?

The biggest-number offer often loses on these. Take the offer that wins on these.

You have 40 years ahead. Compound wisely.

The last word¶

When the offer email lands in your inbox in month 12 or 13, remember two things.

First: you earned it. Not because you were special, but because you showed up on the days it was hard. That’s what earned it. Own that.

Second: the roadmap doesn’t end here. It never ends. Month 14 is another lab notebook entry. Month 20 is another benchmark writeup. Month 30 is another PR. The 13 months were the initiation. The career is the rest of your life.

And on the day you sign the offer, brother — take that dinner off. The whole evening. Real food, real people, phone away. Celebrate the shift from becoming to being one. You earned that too.

Go write the sprint plan.