Yeh Kitab Jin Roles Ki Training Deti Hai
Market titles itni tezi se invent kar raha hai ke unhein define karna mushkil ho gaya hai. In mein se aksar titles aik hi discipline ke mukhtalif depths hain: woh discipline jo yeh kitab sikhati hai. Yeh map hai, aur yeh bhi ke kitab har role ki taraf aapko exactly kahan tak le jati hai.
Yahan hum naye agentic AI era ke roles define karte hain: woh jobs jo is liye exist karti hain ke companies ab AI Workers manufacture, run, aur govern karti hain. Entries is hisaab se sorted hain ke work asal mein kaise cluster hota hai, aur har aik ke saath verdict honest scope line hai: yeh kitab aapko us role ki taraf kahan tak le jati hai, aur kahan certification tracks aage handle karte hain. Verdicts names se zyada important hain. Jahan kitab rukti hai, woh keh deti hai.
Har shakhs same Foundations se shuru karta hai, browser skills jo kisi bhi agent work se pehle har reader ko chahiye hoti hain. Isi floor par general agent use ke do modes hain. Mode 1 general agent ko apna kaam tezi se karne ke liye use karna hai, aik proficiency jo har reader ko chahiye, job title nahin. Mode 2 AI Workers manufacture karna hai jo aap ke liye kaam karte hain, aur job titles yahin aate hain. Map Foundations floor aur Mode 1 Practitioner se shuru hota hai, phir Mode 2 roles ki taraf jata hai, jo is ka lagbhag sara hissa hain.
Vocabulary mein naye hain (Digital FTE, SKILL.md, Agent Factory)? Pehle Thesis aur Glossary se shuru karein; yeh page inhein known maan kar chalta hai.
Poora map aik nazar mein: core pipeline, jo usay extend aur support karta hai, kitab kahan rukti hai, aur neeche ki baseline.
Woh baseline jahan se sab shuru karte hain
Foundations: floor, dono modes se pehle. Har reader same tareeqe se shuru karta hai, browser tab mein, Foundations par: prompting kaise karein, agentic work ki do document languages, woh code kaise commission karein jo aap khud kabhi nahin likhte, skills aur connectors, aur AI era mein kaise sochna hai. Koi mode nahin, koi role nahin, install karne ke liye kuch nahin. Yeh woh floor hai jis par poora map khara hai. Jahan sab shuru karte hain, title nahin.
Mode 1 Practitioner: title nahin, proficiency hai. Isi floor par aap general agent ko apna kaam tezi se karne ke liye use karte hain: reason karna, likhna, code karna, analyze karna, plan karna, outcome ship karna, aur session close karna. Yeh Mode 1 hai, aur kitab isay sab ke liye train karti hai: engineers ke liye Claude Code ya OpenCode ke zariye, domain experts ke liye Claude Cowork ya OpenWork ke zariye, Seven Principles of General Agent Problem Solving ke under. Yeh pehla mode hai jo har reader neeche diye gaye Mode 2 roles se pehle run karta hai, aur yeh aapko us job mein sharper banata hai jo aap ke paas pehle se hai, naya title nahin deta. Pehla mode jo sab run karte hain, title nahin.
Generalist core
Yeh core roles aik single pipeline ki tarah chalte hain, intent se production tak: Outcome Architect (kya) -> Digital FTE Builder (build) -> AI-Native Company Architect (system) -> Cloud AI Engineer (run). Isay apni company ke andar chalayen to yeh chaar roles hain; client ki company ke andar chalayen, end to end aik embedded, vendor-neutral engineer carry kare, to yeh Forward Deployed Engineer hai. Map par baqi sab kuch is line ko support, extend, ya bound karta hai.
Chaar roles aap ki apni company ke andar line run karte hain; aik embedded engineer wahi line client ki company ke andar carry karta hai.
Outcome Architect: intent own karta hai, execution nahin. Agent era mein work teen hisson mein split hota hai: intent, execution, verification. Worker execution own karta hai; yeh role intent own karta hai. Yeh decide karta hai ke Worker kya achieve kare, spec likhta hai jo usay pin down karti hai, "correct" ka matlab set karta hai, aur prioritize karta hai ke kaun se Workers banaye jayen. Builder kaise ka jawab deta hai, us se pehle yeh human kya aur kyun ka jawab deta hai. Jahan Strategist track client-facing discovery aur ROI own karta hai, Outcome Architect internal Worker roadmap aur us ke peeche specs own karta hai. Kitab isay directly train karti hai: spec-driven development asal mein woh discipline hai jahan aap intent itna clear likhte hain ke Worker ko us ke against hold kiya ja sake. Isay train karti hai: woh discipline jis par poora method tikta hai.
Digital FTE Builder: unit product, end to end built. Market isay AI Engineer kehta hai, aik catch-all title jo AI components se applications banane aur AI coding agents drive karne wale shakhs ke liye use hota hai. Is kitab ka naam sharper hai, kyun ke jo cheez aap build karte hain woh bhi sharper hai: Digital FTE, woh unit jis se poori company assemble hoti hai. Yeh kitab ka primary graduate hai. Yeh full spine train karti hai: spec-driven development, SKILL.md authoring, agent architecture, tool aur MCP interfaces, evaluation, aur human oversight, deployment itna ke ship kar saken, aur gehri production depth Cloud AI Engineer ke liye chhorti hai. Isay end to end train karti hai.
AI-Native Company Architect: company design karta hai, single Worker nahin. Poori enterprise: Two-Layer Model, management layer, workforce, woh nervous system jo in ke darmiyan events carry karta hai, aur system of record jis ke against yeh sab run hota hai. Agent Factory woh process hai jo yeh architect practice karta hai; AI-Native Company woh product hai jo woh ship karta hai. Kitab is ka canonical source hai. Paanch-quarter Certified Agentic AI Architect program is ki credential hai. Full train hota hai; Architect track se certify hota hai.
Cloud AI Engineer: production mein AI Worker aur AI-Native Company run karne wala. Digital FTE build karna kaam ka aik half hai; usay reliably run karna doosra half hai. Aur us AI-Native Company ko run karna bhi jis se woh belong karta hai. Jahan AI-Native Company Architect enterprise design karta hai, yeh role usay operate karta hai: Workers, management layer, aur nervous system ko real cloud infrastructure par deploy aur scale karna, ship ke liye Azure Container Apps, durable execution ke liye Inngest, aur scale ke liye Dapr aur Kubernetes. Yahin system prototype se company banta hai jis par organization depend kar sakti hai. Isay end to end train karti hai.
Do roles jinhein lagbhag koi aur train nahin karta
Subject Matter Expert as Skill Author: woh role jiska naam market ne abhi nahin rakha. Accountant, lawyer, ya supply-chain expert jo judgment ko SKILL.md mein encode karta hai aur Digital FTE ka knowledge engine banta hai. Market lists aksar is role ko miss karti hain kyun ke woh abhi bhi AI work ko engineering-only samajhti hain. Yeh kitab domain judgment ko aisi cheez samajhti hai jise author, test, aur deploy kiya ja sakta hai. Yeh un do roles mein se aik hai jinhein lagbhag koi aur train nahin karta. Isay full train karti hai: judgment andar, working agent bahar.
Forward Deployed Engineer (FDE): vendor-neutral version jo market ko nahin milta
FDE asal mein kya karta hai. Aksar software engineers headquarters mein product build karte hain aur us customer se kabhi nahin milte jo usay use karta hai. FDE iska ulta karta hai. Woh customer ke real workplace par jata hai, kaam karne wale logon ke saath baithta hai, un logon ki real problems samajhta hai, aur wahin on-site apni company ke platform se solutions build karta hai. Demo nahin. Slide deck nahin. Working software jo customer ke real environment mein run karta hai.
Isay doctor ki misaal se samjhein: aik doctor doosre shehar se aap ka chart parhta hai, aur doosra doctor room mein baith kar aap ko examine karta hai aur treatment foran shuru karta hai. FDE doosra doctor hai.
Palantir, aik major data analytics company jo governments aur large enterprises ke liye software banati hai, ne early 2010s mein yeh role create kiya, aur pehle inhein "Deltas" kaha.1 2016 ke qareeb tak Palantir ke paas regular software engineers se zyada FDEs the, kyun ke us ke customers, government agencies aur large traditional enterprises, ko on-site aise shakhs ki zaroorat thi jo startup mentality ke saath internal bureaucracy cut through kar sake. Palantir ka difference explain karna sab se clear hai: regular developer one capability, many customers par focus karta hai (aik feature build karo, sab ko ship karo), jab ke FDE one customer, many capabilities par focus karta hai (aik client ke saath embed ho, jo kuch chahiye solve karo). Job description scope capture karti hai: yeh startup CTO jaisa dikhta hai. High-stakes projects par aap start se finish tak sab own karte hain.
Yeh Solutions Architect ya Sales Engineer jaisa nahin. Solutions Architect advise karta hai: demos run karta hai, whiteboards par solutions design karta hai, aur sample data se proof-of-concept prototypes build karta hai taake prospect sign kare. Deal close hone ke baad involvement aam tor par kam ho jata hai. FDE wahan se pick karta hai jahan Solutions Architect chhorta hai. Woh customer ki infrastructure par, real data ke saath, production code likhta hai, aur tab tak rehta hai jab tak customer ko real value na mil jaye. Simple test: agar role customer-specific work ko production mein waqai function karwane ke liye accountable hai, to woh FDE ke qareeb hai. Agar role product ko prove ya explain karne ke liye accountable hai, to woh solutions architect ke qareeb hai.
Aik real example: OpenAI ki deployment team John Deere ke saath embed hui, jo lagbhag 190 saal purani farming company hai, taake planting-season problem ground par solve ki ja sake. Har farmer ke apne past usage se kaam le kar unhon ne AI-powered recommendations build karne mein madad ki ke See & Spray, John Deere ka targeted weed-control system, farmer ko kitna kam chemical chahiye hoga. John Deere ne is kaam ko chemical use mein lagbhag 70% kami aur customer engagement mein sixfold lift ka credit diya.2 Deadline product roadmap nahin, planting calendar tha. Yeh FDE job aik line mein hai: real production software, customer ki duniya mein built, aur tab shipped jab customer ko waqai zaroorat ho.
FDE woh jagah hai jahan code, product, aur customer milte hain: software engineer ki build capability, platform engineer ki product instincts, aur solutions architect ki customer read, aik aise shakhs mein jo customer ki duniya mein build karta hai, headquarters mein nahin.
Kyun har AI company ab FDEs chahti hai. 2025 ke pehle teen quarters mein FDE job postings 800% se zyada barh gayin.3 Salesforce ne apni Agentforce platform support karne ke liye dedicated FDE team banayi.4 OpenAI ne "Deployment Company" create ki, aik majority-owned subsidiary jise investor consortium se lagbhag $4 billion backing mili, aur jo largely enterprises ko FDEs staff karne ke gird bani.5 Is sab ke peeche wajah simple hai: 2025 MIT Media Lab study (Project NANDA) ne paya ke custom enterprise AI pilots mein se lagbhag 95% measurable return nahin dikhate.6 Is liye nahin ke AI kaam nahin karta, balki is liye ke usay company ke messy, real-world systems mein fit karna bohat hard hai. FDEs isi gap ko close karne ke liye exist karte hain. Yeh wajah hain ke Palantir late 2024 tak $136 billion market cap cross kar gaya, Lockheed Martin se aage nikal gaya,7 aur ab har AI company model replicate karna chahti hai.
Vendor lock-in problem. Catch yeh hai. Palantir ka har FDE Palantir ki platform par build karta hai. OpenAI ka har FDE OpenAI ke models par build karta hai. Salesforce ka har FDE Salesforce ke tools par build karta hai. Engineer client ki company mein gehrai tak jata hai, us aik vendor ka product har cheez mein wire karta hai, aur chala jata hai. Baad mein switch karna painful aur expensive hota hai, jaise plumber jo sirf aik brand ki pipes install karta hai: plumbing kaam karti hai, lekin aap walls tore baghair doosra plumber hire nahin kar sakte. Jaisa Andrew Ng ne The Batch mein note kiya,8 clients ko aise FDEs dhoondhne mein mushkil hoti hai jo single vendor se tied na hon, kyun ke vendor ke liye role ka point hi client ko lock in karna hai.
Yeh kitab woh FDE train karti hai jo market maangta rehta hai lekin dhoondh nahin pata. Yahan ka method kisi vendor se bound nahin. Is kitab ka graduate full pipeline carry karta hai (intent spec karo, Worker build karo, system design karo, production mein run karo) client organization ke andar, baghair client ko kisi single platform mein lock kiye. Agle quarter behtar model aaye, ya agle saal cheaper runtime ship ho, to aap switch karte hain. Client choice ki freedom rakhta hai, aur aap woh discipline rakhte hain jo kisi bhi stack par kaam karta hai. Aik honest tradeoff bhi naam lena chahiye: vendor ka FDE heavily subsidized hota hai, kabhi free bhi, kyun ke vendor lock-in se wapas earn karta hai; vendor-neutral FDE client ya independent firm pay karti hai. Yeh feature hai, bug nahin: client ab optionality khareed raha hai, baad mein switching costs nahin de raha.
Yeh doosra role hai jise lagbhag koi aur train nahin karta. Aur claim ko exact rakhne ke liye: kitab vendor-neutral FDE ka technical core train karti hai, woh half jo market ko nahin milta. Doosra half, client discovery, prioritization, ROI framing, aur unrealistic ask par push back karne ki discipline, Certified Agentic AI Business Strategist track ka hai. Technical core train karti hai; consulting layer Strategist track mein rehti hai.
Supporting roles
Har pipeline ko log chahiye jo work check karein, rules set karein, aur responsibility lein. Yeh teen roles yeh kaam karte hain.
Evals Engineer: woh shakhs jo AI Workers ko live jane se pehle crash-test karta hai. Aap car ko crash-test ke baghair ship nahin karenge. Medicine ko clinical trials ke baghair release nahin karenge. AI Worker jo real logon aur real paisay ko affect karne wale decisions leta hai, usay bhi wahi discipline chahiye. Evals Engineer woh tests design karta hai: kya Worker sahi answer deta hai? Kya woh gracefully fail karta hai jab usay aisi cheez mile jo us ne kabhi nahin dekhi? Kya woh diye gaye boundaries ke andar rehta hai? Yeh end par bolt-on kiya gaya afterthought nahin. Yeh har chapter mein built in hai. Core curriculum, add-on nahin.
AI Governance Officer: decide karta hai ke AI kya kar sakta hai. Company mein har employee ki limits hoti hain. Junior accountant $500 tak expenses approve kar sakta hai, us se upar manager signature chahiye. Bank teller deposit process kar sakta hai, loan approve nahin kar sakta. AI Workers ko bhi wahi structure chahiye. Governance Officer company level par woh rules likhta hai: AI apne aap kya decide kar sakta hai, kya human approval ke liye jana chahiye, aur AI ko kya kabhi touch nahin karna chahiye. Woh regulations ki mapping bhi handle karta hai: bank mein fair lending rules, hospital mein patient privacy, Europe mein data residency laws. AI-Native Company Architect woh system build karta hai jo yeh rules enforce karta hai; Governance Officer decide karta hai ke rules mein kya likha ho. Kitab yeh framework discipline directly train karti hai; aap ki industry ki specific regulations woh inputs hain jo aap late hain. Governance framework train karti hai; aap ki jurisdiction ke rules aap supply karte hain.
Digital FTE Supervisor: woh human jiska naam line par hai. Jab AI Worker claim process karta hai, contract draft karta hai, ya transaction flag karta hai, kisi ko accountable hona parta hai. Woh Supervisor hai. Woh human-in-the-loop hai: reviewer jo work check karta hai, manager jo output approve karta hai, woh naam jahan audit trail point karti hai jab kuch ghalat hota hai. Yeh Worker build karne wala shakhs nahin. Yeh woh shakhs hai jo usay day to day run karta hai, jaise shift manager team run karta hai. Isay train karti hai.
Jahan kitab jaan boojh kar rukti hai
LLMOps Engineer: model tak, model itself nahin. Production mein agents run karna Cloud AI Engineer ka job hai, aur kitab isay train karti hai. Kitab fine-tuning hands-on bhi train karti hai, lekin last resort ke tor par, default nahin. Fine-tune aap ke system ko aik model snapshot se bind karta hai aur us optionality ki cost lagata hai jise poora method protect karta hai, is liye aap isay sirf tab use karte hain jab prompting, context, tools, aur retrieval waqai kam par jayen. Hard stop model itself build karna hai: foundation model ko scratch se pre-train karna scope se bahar rehta hai, kyun ke woh capability commoditize ho rahi hai. Fine-tuning aur model ke gird ops train karti hai, foundation models build karna nahin.
Harness Engineer: runtime jo aap use karte hain, woh nahin jo aap build karte hain. Harness agent runtime hai, OpenAI Agents SDK, Claude ke managed agents, aur aisi cheezen, jo agent loop run karti hain, state manage karti hain, aur tool calls execute karti hain. Kitab aapko inhein fluently use karna aur in ke darmiyan portable rehna train karti hai, kyun ke aap ki discipline kisi bhi winning runtime se zyada long-lived hai. Runtime itself build karna job nahin. Kisi bhi runtime ko use karne wale operator ko train karti hai, usay build karne wale engineer ko nahin.
AI Data Engineer: agent-facing data layer. System-of-record work agent-facing data layer ko touch karta hai: Postgres, pgvector, aur MCP woh spine hain jis se agent read karta hai. Classic pipeline aur warehouse engineering adjacent hain, central nahin. Agent-facing data layer train karti hai, general data engineering nahin.
Pattern hi signal hai. Agent era work ko bohat se roles mein phailata hai, aik mein nahin: Workers build karna, unhein run aur govern karna, unhein judgment sikhana. Map point hai: dekhein aap abhi kahan khare hain, aur yeh kitab aapko wahan se kitna aage le jati hai.