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02. Career Strategy

Target roles and comp bands

Apply now

Role Base band (LPA) Why it fits
Lead / Applied AI Engineer (Agents) ₹40-70 Best immediate fit
Senior Product Engineer — AI Platform ₹35-60 Strong bridge role
Computer Vision / Vision Platform ₹35-60 Good only if still production-oriented
Founding Engineer ₹40-60 base + 0.5-2% equity Best for scope + equity + founder access

Apply after stronger infra proof

Role Base band (LPA) Trigger
AI Infra / Cloud Platform ₹45-75 K8s + serving artifact shipped
Edge AI / on-device AI ₹40-70 Edge / on-device (e.g. quantized VLM) artifact shipped

Compensation rules

Scenario Floor Target
Track A senior pivot ₹35 LPA base ₹45-60 LPA
Founding Engineer ₹40 LPA base + 0.5% equity ₹50-60 LPA + 1-2% equity
Stretch — ₹70-80 LPA base
  • Remote or Gurugram first.
  • 4-year vesting + 1-year cliff is standard.
  • Ask for strike-price transparency.
  • Ask for double-trigger acceleration when relevant.
  • Keep notice period at 30 days max.

Company buckets

  • Hot: UnifyApps, Spyne, Rezo, Sarvam, Krutrim, Leena AI, Netomi, CoRover, Algo8, Awiros, Conxai, Staqu, Netradyne.
  • Warm: TrueFoundry, NVIDIA cloud, EdgeImpulse, Latent AI, Detect, Flutura, Augury.
  • Use selectively: Yellow.ai, Haptik, Skit.ai, Uniphore, large generic product companies.
  • Skip: robotics-heavy, research-heavy, generic senior SWE roles with no AI signal.

Search cadence

90-day plan

Window Focus
Month 1 Add evals to current chatbot; apply to top Track A companies; start founder/CTO outreach
Month 2 Build multi-framework + retrieval + MCP artifacts; activate more founder-lane conversations; start edge marker only if still high-conviction
Month 3 Add serving / infra proof; push toward converging loops; reframe if no credible offers

Weekly cadence

Day Activity
Mon AM Identify 3-5 applications; tailor bullets
Mon-Fri AM 1 application + 1 follow-up + 1 cold DM
Mon-Fri PM Skill / artifact block
Saturday Ship the week's public artifact
Sunday Post, distribute, plan next week
  • Rule: Sat = ship, Sun = post.
  • Do not trade shipping time for more reading.
  • Track every touch in Portfolio.xlsx.

Founder-lane rules

  • This lane comes from proof + direct outreach, not portals.
  • Target examples: Conxai, GobbleCube, Algo8, Spyne, UnifyApps, Awiros, Sarvam, Krutrim, CoRover, EdgeImpulse, Latent AI, TrueFoundry.
  • One personalized DM per business day is enough.
  • Lead with proof, not curiosity.
  • Attach a repo link, short demo, or screenshot.
  • Follow up once after 7 days, then move on.

JD filter

JD mentions... Likely lane Action
LangGraph, LLM APIs, RAG, agents, prompt design Applied AI Apply
Evaluation, reliability, oversight, safety Reliability / evals Apply
Kubernetes, vLLM, Triton, model serving, observability AI platform Apply after stronger infra proof
Founding engineer, first technical hire, 0→1 Founder lane DM founder directly
Fine-tune, train from scratch, distributed training Research / deep ML Usually skip
ROS2, robotics, SLAM, perception stack Robotics Skip
Publications, novel architectures, research-heavy ML Research Skip

Outreach templates

Cold founder DM

Hi [FOUNDER] — I'm a [senior / SDE2] engineer who ships production AI ([your headline artifact — e.g. a LangGraph agent + retrieval pipeline]). [COMPANY] looks like the closest production-AI fit I've seen. I built a [SHORT DEMO] relevant to [SPECIFIC THING YOU DO] — happy to share if useful.

Follow-up after 7 days

Hi [NAME], following up on my application from [DATE] for [ROLE]. Since then I shipped [NEW ARTIFACT] — sharing in case it's useful: [LINK].

Post-interview thank-you

Hi [NAME], thanks for the conversation. The part on [SPECIFIC TOPIC] was especially useful — I went home and [read / built / sketched something]. Sharing here: [LINK].

Negotiation playbook

Sequence

  1. Get the offer in writing.
  2. Pause for 48-72 hours.
  3. Counter on at least two dimensions.
  4. Use other active conversations as leverage.
  5. Get the final offer in writing.
  6. Accept or decline cleanly.

Negotiate on

  • Base salary.
  • Equity.
  • Joining bonus.
  • Notice buyout.
  • Title.
  • Remote flexibility / relocation.

Red flags

  • Offer not in writing.
  • Equity or strike price kept vague.
  • Title compression.
  • Rigid start date with no notice flexibility.
  • "Take it or leave it" pressure too early.

Non-negotiable rule

  • Always have 2 active offer conversations before accepting any offer.

4-year horizon

Year Theme Likely destination
Year 1 Pivot + agent foundation Lead / Applied AI Engineer or Founding Engineer
Year 2 Platform specialization Staff / Lead or deeper founding-equity path
Year 3 Multiplier choice Principal / leadership / founder route
Year 4 Optionality cashed in Multiple strong paths instead of one narrow lane
  • Bet on seams AI cannot erase easily: reliability, vertical depth, infrastructure, and human leadership.
  • Ship one production-grade public artifact per quarter at minimum.

Specialization choices

Option Optimizes for 2028 ceiling Choose if...
Reliability / evals Rare senior moat; low career risk ₹70-100L You like debugging, precision, failure-mode thinking
Multi-agent systems Frontier architecture depth ₹70-110L You like orchestration, complexity, bleeding-edge systems
AI infra / MLOps Highest IC comp ceiling ₹80-120L You like platform work, serving, K8s, infra leverage
Founding engineer / equity Scope + ownership + upside Variable You want breadth and accept risk

Default tie-breakers

  • Risk-averse + comp-first -> AI infra.
  • Premium specialist + lower variance -> reliability.
  • Frontier-heavy + architecture-first -> multi-agent.
  • Equity-seeking + broad scope -> founding engineer.
  • Force the choice by Month 18.

Canary signals

Personal

  • Application reply rate.
  • Inbound recruiter / founder DMs.
  • GitHub engagement.
  • LinkedIn post quality and DM conversion.
  • Comp band of inbound opportunities.

Market

  • LangGraph health vs vendor SDK adoption.
  • MCP adoption.
  • Foundation-model capability jumps.
  • Eval benchmark saturation.
  • Funding and layoffs among target AI companies.

Act immediately when

  • Personal metrics drop and the market deteriorates.
  • A target company announces layoffs.
  • Strong inbound appears in an unexpected but attractive lane.

First 90 days on the job

Weeks 1-2

  • Read the code.
  • Run the eval suite, or note its absence.
  • Study traces, incidents, and postmortems.
  • Meet every teammate.
  • Do not start with a big rewrite.

Weeks 3-4

  • Ship one small, safe thing: bug fix, prompt improvement, tool, or observability addition.
  • Pair on early PRs.
  • Watch the change in production.

Month 2

  • Own one feature end-to-end.
  • Write a short design doc.
  • Build with eval discipline from day 1.
  • Ship, monitor, iterate.

Month 3

  • Contribute to architecture after earning trust.
  • Good first larger proposals: eval framework, HITL pattern, migration path, new capability.

Avoid

  • Fixing everything in week 1.
  • Suggesting a framework migration before understanding trade-offs.
  • Skipping 1:1s.
  • Staying silent in design discussions.
  • Overcommitting in the first sprint.

Public artifacts tracker

# Artifact Type Tied to Status Distribution Target date Notes
1 MNIST classifier writeup Repo + post Module 01 TODO LinkedIn, GitHub TBD
2 Transformer from scratch Repo + post Module 04 TODO LinkedIn, HN TBD
3 Fine-tuning case study Repo + post Module 06 TODO LinkedIn, blog TBD
4 RAG chatbot Repo + post Module 08 TODO LinkedIn, blog TBD
5 Tool-calling agent Repo + post Module 09 TODO LinkedIn, blog TBD
6 MCP demo Repo Module 10 TODO LinkedIn TBD
7 LLM judge Repo + post Module 11 TODO LinkedIn, blog TBD
8 Reasoning model exploration Repo + post Module 12 TODO LinkedIn TBD
9 Multimodal demo Repo + post Module 13 TODO LinkedIn TBD
10 Diffusion model writeup Repo + post Module 14 TODO LinkedIn TBD
11 Capstone Repo + post Module 15 TODO LinkedIn, blog, HN TBD
12 Engineering principles essay Post Module 16 TODO Blog TBD
13 MLOps serving writeup Repo + post Module 17 TODO LinkedIn, blog TBD
  • Add ad-hoc artifacts for interview-driven work, OSS PRs, talks, and writeups.
  • Goal within 90 days: page-1 search results should show blog/LinkedIn, 2-3 polished repos, 1-2 strong technical posts, and 1-2 OSS signals.

Distribution plan

Channel Audience When
LinkedIn Recruiters, hiring managers Always
Personal blog Long-form home, SEO Always
HN / Lobsters Technical peers, viral upside Strong deep technical pieces
Twitter / X AI community Break strong posts into a thread
r/MachineLearning / r/LocalLLaMA Topic-specific peers Topic-fit posts
Newsletters / podcasts Authority building After a body of work exists

Content bar

  • Specific build notes, not generic hot takes.
  • Real numbers: latency, cost, accuracy, token counts, throughput.
  • At least one failure mode and what changed.
  • Repo, notebook, screenshot, or public artifact attached.
  • Personal voice, not tutorial voice.
  • End with a hook or question.

Cadence

  • Saturday: ship the repo / README.
  • Sunday: LinkedIn post.
  • Mid-week: long-form blog post if deserved.
  • Monthly: one OSS issue or PR.
  • Quarterly: one talk proposal.

Common pitfalls

  • Over-investing in one framework.
  • Shipping toy demos with no evals.
  • Chasing every model release.
  • Deferring production patterns until after a failure.
  • Reading too much; shipping too little.
  • Posting generic AI hot takes.
  • Comparing against ML researchers.
  • Spending too long on credentials and courses.
  • Using apologetic positioning.
  • Not specializing by Year 2.
  • Accepting first offer below floor.
  • Not negotiating.