Working In AI
The roles that exist, what they pay, and how people get in without a famous degree.
- Level
- Nothing assumed
- Lessons
- 85
- Reading time
- 731 min
- Price
- Free, no sign-up to read
A plain-spoken guide to building a career in and around AI: the roles that actually exist and what they look like on an ordinary Tuesday, where a degree matters and where it does not, how to build a portfolio with no job history, what to do when the courses run out and you still freeze at an empty file, how hiring really works in India, Nigeria, Brazil, Europe and the US, what the money looks like in local terms, how freelancing and contracting actually pay, and which parts of this work are most exposed to automation. No hype, no doom, and no pretending that any of it is the same everywhere.
Opens after the AI at Work exam
Sign in, finish that course, and pass its exam. You can read this syllabus meanwhile.
Go to AI at WorkModule 1
The terrain: who hires, for what, and where you fit
Before you choose what to learn, look at the market you are actually in — the eight kinds of employer, how a job advert encodes the real job, and what a credential buys. This module comes first because most wasted years are caused by aiming at the smallest employer category with the wrong evidence.
By the end you can
Name the employer types that hire for AI work in your own market, decode a job advert into the role it really is, and commit to one lane with a plan sized to the hours you actually have.
- 1The jobs, described as they feel on a TuesdayMost AI work is moving messy data and checking outputs, not training models.
- 2The eight kinds of employer, and how many of eachNearly every AI-adjacent job sits inside an ordinary organisation that uses software, not inside a company that builds models.
- 3Geography, time zones, and the office you have never heard ofWhere you sit decides which employers can legally and practically hire you, and time-zone overlap filters candidates harder than any skill requirement in the advert.
- 4The work that trains the modelsAnnotation and evaluation work is a real door into the field, but its value is the failure-mode intuition and data-quality vocabulary you take out of it, not a promotion track inside it.
- 5Reading a job advert for what it actually isThe tools list and the responsibilities tell you the real job; the title and the years-of-experience line usually do not.
- 6Who sits around you, and how work arrivesThe organisation's shape and data maturity determine your daily work more than the job title does, and both can be diagnosed with one question in the interview.
- 7Who actually needs a degreeA degree opens doors in some markets; evidence that you can build is what keeps you inside.
- 8Choosing a lane you can defend, and changing it laterChoose the lane where you can produce evidence fastest, because lanes are adjacent and moving between them costs one project, not one degree.
- 9Levels, titles, and why "senior" means nothing on its ownLevels are set by how much ambiguity you absorb and how far your work travels, so negotiate the level rather than the salary and read adverts for scope rather than titles.
- 10A year on six hours a weekProgress comes from a small weekly amount that survives bad weeks, because every gap longer than about ten days costs a session to re-enter.
Module 2
The roles, one at a time
One overview lesson cannot tell you whether you would enjoy being a data engineer. This module takes ten roles and gives each its own hour — the ordinary week, the thing you produce, the tools with their free equivalents, the two routes in, and the failure that ends people's first year in that seat. Read the three that sound closest and skim the rest, because knowing what you are not applying for is half of choosing.
By the end you can
Describe one role's ordinary week, the artefact it produces, the stack it runs on and two realistic routes into it in enough detail that somebody doing that job recognises their own week in your description.
- 11The data analystAn analyst's value comes from owning what the words mean and being accountable for the number, not from producing charts faster.
- 12The analytics engineerAnalytics engineering makes data trustworthy by treating transformations as idempotent, version-controlled, tested code, and its only real success metric is whether other people build on your tables.
- 13The data engineerData engineering is judged on availability and silence, so idempotency, backfills and loud failure matter more than any tool on the CV.
- 14The machine learning engineerMost of a machine learning engineer's work is the system around the model, and the defining bug is a feature that exists in training data but not at prediction time.
- 15The AI engineerAn AI engineer's core skill is separating retrieval failures from generation failures and trading quality, cost, latency and risk with actual numbers.
- 16The platform and MLOps engineerPlatform and MLOps work is judged by other teams' delivery speed and by adoption, so build one well-supported road with a real team before generalising it.
- 17The applied scientist and research engineerApplied research is run on time-boxed questions against a properly tuned baseline, and its most valuable habit is writing up what did not work.
- 18Trust, safety and the policy side of AITrust and safety is a chain from policy to taxonomy to detection to appeals, and every threshold is an explicit choice about which of two harms you would rather cause.
- 19The AI product managerAn AI product manager's job is to state what decision changes, what being wrong costs, and what the fallback is, then insist on a measured number instead of an impression.
- 20The forward-deployed and solutions engineerForward-deployed work makes a product work inside one organisation, and its career value is the loop back into the product — which fails the moment each customer gets a permanent fork.
Module 3
The evidence: what gets tested, and how to prove you have it
Four skills carry almost every entry-level offer in this field — SQL, working Python, version control and evaluation — and one artefact converts them into a hire: a written project that shows judgement under mess. This module builds both, and covers the spreadsheet fluency no syllabus lists and every organisation runs on.
By the end you can
Produce one written case study of your own work — with the mess you found, the decisions you rejected, and a number from a test set you built by hand — that a reviewer can judge in ninety seconds.
- 21When the courses end and you still freezeYou learn to build by finishing small things alone, not by watching bigger things being finished.
- 22SQL, and the six query shapes that cover the jobSQL is tested in almost every data interview because it is fast to test and impossible to fake, and six query shapes cover nearly all of the work.
- 23The employable half of PythonThe paid part of Python is reading other people's code, handling files and failures, and writing a script that somebody else can run.
- 24The statistics that come up, and the ones that never doPrecision improves only with the square root of sample size, so most business tests were never large enough to detect the effect they were run to find.
- 25Git, and what a reviewer sees before your codeYour repository is read as evidence that you can work with other people, and the commit history is read before the code is.
- 26The terminal, the container, and a cloud bill of zeroCloud costs are dominated by resources that bill for existing rather than for working, so set a budget alert first and prefer things that scale to zero.
- 27The spreadsheet skill nobody puts on a syllabusThe organisation's real numbers live in spreadsheets, and being able to meet people there is what gets you access to the questions worth answering.
- 28Evaluation: deciding what "correct" means, in advanceAnyone can produce an AI output; the scarce and durable skill is defining what counts as correct before you look, and then measuring it.
- 29A portfolio when you have never been paid for thisA portfolio proves judgement under mess, not that you can finish someone else's tutorial.
- 30Where portfolio data comes from, and what you may do with itA dataset's licence, its provenance and whether it describes people decide what you may do with it, and the absence of a licence means you have no permission rather than a free hand.
- 31Writing up a project as a decision recordWrite the project as a record of decisions — the mess, the choice, the number, and what it still gets wrong — because that is what a reviewer is reading for.
Module 4
Getting seen: the routes that put a person in front of your work
Applying cold is the most common strategy and the weakest one. Every route that works has the same shape — a human sees your work before, or instead of, an application landing in a pile. This module covers the paper, the message, the job beside the data, and the first person who pays you.
By the end you can
Run a search that puts your work in front of named people rather than into application queues, and identify the bridge role or first paid job you would take to get inside.
- 32How people actually get inMost people enter this field sideways, from an adjacent job or through someone who vouches.
- 33Running the search as a funnelA job search is a funnel with measurable conversion at each stage, and the route in changes the top-of-funnel rate by roughly ten times, which no amount of extra applications can compensate for.
- 34A CV that survives a thirty-second readA CV is scanned for scope, outcome and a number; formatting exists only to make those findable fast.
- 35The profile a stranger checks in ninety secondsThree public surfaces each answer one question in ninety seconds, and a reviewer who compares them treats inconsistencies as reasons to stop reading.
- 36Writing to strangers, and what to sayA short, specific message about their problem, sent to the person who does the work, outperforms fifty applications.
- 37Publishing, and the inbound it createsPublished writing works because it is found by strangers months later, which rewards narrow, specific answers to real problems over broad guides written from a week's knowledge.
- 38A merged pull request as evidenceA merged pull request proves you can work inside someone else's codebase and take review, and the reproduction of a bug is often a more welcome contribution than the fix.
- 39The job next to the dataTake the job beside the data and become the person who answers with numbers, because an internal move skips the entire screening machine.
- 40The first client, and how not to lose money on themThe first paid job converts you from a learner into someone with a reference, so make it small, write the scope down, and pick work you have already done once.
Module 5
Using AI in your own search
Both sides of the hiring table now use models, and most advice about it is sold by people with something to sell — CV scores, auto-apply subscriptions, interview coaching. This module is the mechanics: what an applicant tracking system actually does, why volume applying stopped working, where a model genuinely saves you hours, where using one destroys the evidence you were trying to produce, and what you should never paste into somebody else's server.
By the end you can
Use a model for the mechanical parts of a search — tailoring, research, rehearsal, gap analysis — while stating correctly what an applicant tracking system does and does not do, and keeping your own and an employer's confidential material out of a third party's logs.
- 41What it genuinely helps with, and what it quietly ruinsUse a model wherever you can verify the result and the result is not itself the evidence of your ability; the moment the output is the thing being judged, using one removes what you were trying to prove.
- 42What an applicant tracking system actually doesAn applicant tracking system parses, stores and filters — the automatic rejection happens on the form's knockout questions, not through a secret score applied to your CV.
- 43Rewriting a CV against an advert, and the lineTailoring is translating true things into the employer's vocabulary, and the reason not to cross into invention is mechanical: titles and dates get verified and tools get tested.
- 44Why volume stopped working, on both sidesGenerated applications made volume cheap, employers responded by raising the cost of being considered, and the result is that a warm approach is now worth roughly twenty cold applications rather than seven.
- 45Your CV, their take-home, and the termsBefore pasting anything, establish whose data it is; for material that is not yours, a small model running locally removes the question rather than managing it.
- 46Forty minutes of research, and how to check itResearch is aimed at how the organisation makes money, where the role sits and what remains uncertain, and every fact a model gives you needs a source you actually open.
- 47Recorded and machine-scored interviewsA one-way interview is an elimination stage scored mostly from a transcript, so structure, audio quality and a first-sentence answer matter more than anything you would optimise in a live conversation.
- 48Rehearsing with a model that wants to please youInterviews are lost in production rather than recognition, so rehearsal must be out loud, one question at a time, with the model instructed to press on vagueness rather than encourage.
- 49Building a project you can still defendType the parts a reviewer would ask "why" about, delegate the rest, and never move past generated code you cannot explain — because generated data code fails by being plausible rather than by breaking.
Module 6
The interview, from first screen to signed offer
Hiring processes in this field are five to seven stages, and each one tests something different. Knowing which stage you are in tells you what to say. This module walks the whole sequence — the take-home, the live exercise, the design conversation, the stories, the questions you ask, and the offer.
By the end you can
Walk each stage of a data or AI hiring process knowing what it tests, and negotiate an offer without a competing one in hand.
- 50The stages, and what each one is really testingEvery interview stage tests a different thing, and knowing which stage you are in tells you what to say.
- 51The first call, and the salary questionThe recruiter screen tests availability, motivation and band rather than skill, and the two questions worth spending your last minutes on are the internal level and why the role is open.
- 52The take-home, and how not to lose on itA take-home is graded on judgement and communication, not on completeness, so scope it, time-box it, and write down what you left out.
- 53The live exercise, where silence is scoredIn a live exercise the interviewer is grading your narration, so saying where you are stuck scores better than being quietly stuck.
- 54"How would you build this?" answered wellA design answer is a set of constraints, measurements and failure modes, not an architecture diagram.
- 55"How would you measure it?"Answer a metrics case by naming the decision, one sensitive primary metric, guardrails and a counter-metric — and when diagnosing a sudden drop, check the instrumentation before the users.
- 56Talking about your work without lying or shrinkingPrepare six stories with real numbers and tell them the same way every time; the failure story is the one that decides the round.
- 57Your questions, and what the answers revealYour questions decide whether the job is real: ask about where the data lives, what is in production, and why the role is open.
- 58The offer conversationAlmost everything is negotiable once, politely, in writing — and a first offer is rarely the top of the band.
- 59Pay, rates, and who actually decides themPay is set by employer type, country and your alternatives, far more than by your job title.
- 60What a rejection tells you, and what it does notMost rejected finalists were hireable, because hiring ranks candidates under a budget rather than assessing them absolutely — so read your log in tens, not one at a time.
Module 7
The money, and the paperwork underneath it
An offer is a package, and the headline number is the part that tells you least. This module takes the whole thing apart — what the components actually pay out, why most equity is worth nothing, which contract clauses change what the job is worth, how contractor and employer-of-record arrangements differ from employment, and how to compare two offers with arithmetic. Employment law differs by country and this is not legal advice; what is offered here is the shape of the questions and where to take them.
By the end you can
Take an offer apart into cash, variable pay, employer contributions, equity and contract terms, compare two offers with explicit arithmetic rather than headline figures, and name the clauses and employment structures that materially change what you are being paid.
- 61The headline number is the least useful oneCompare guaranteed annual cash against guaranteed annual cash; a bundled total figure mixes employer contributions and target variable pay into a number nobody receives.
- 62Options, RSUs, and why most equity is worth nothingAn option is only worth the share value minus its strike, and the liquidation preference and the ninety-day exercise window decide most real outcomes long before the share price does.
- 63Notice, bonds, IP and the clauses that follow youContract clauses vary enormously by jurisdiction and the only moment you can change one is before signing, so read for notice, bonds, IP assignment and outside-work restrictions and ask for carve-outs in writing.
- 64Employee, contractor, or an employer of recordEmployment carries 25% to 40% of salary in contributions and protections, so a contract at the same gross is a pay cut, and an employer of record gives you local employment status without the client needing an entity.
- 65Getting paid by a company in another countryForeign pay is reduced by an exchange spread nobody itemises, taxed where you are resident rather than where the client is, and usually benchmarked to your city — so compare it against your real local alternative, not against the employer's home market.
- 66The shape of the routes, and why the rules keep movingImmigration rules move constantly and only official sources are current; the routes that reward planning are points-based systems and the internal transfer, and no legitimate party ever charges a candidate for a job offer.
- 67Two offers, compared with arithmeticCompare guaranteed cash first, then expected cash using historic payout rates, then contributions, avoided spending and the costs of the job — and enter equity in a private company at zero with the upside noted separately.
- 68Raises, promotions and the market rateRaises come from a small annual pool, promotions move your band, and job changes reset you to market — so if the band is the constraint, no amount of performance evidence will fix it internally.
Module 8
Freelancing and contracting, as a business
The first paid job is covered in the module on getting seen. This module is what comes after it — the arithmetic that turns a salary into a rate, where a repeatable pipeline of clients comes from, how to write a scope that prevents the argument, how to get paid by a company whose finance department has never heard of you, and the honest conditions under which working for yourself is the wrong choice this year.
By the end you can
Price your own work upwards from a target income and a realistic utilisation figure, write a proposal whose exclusions prevent scope creep, run a payment process that survives a slow client, and state the conditions under which you should take a salaried job instead.
- 69Utilisation, and why a rate is not a salary divided by hoursOnly half to two-thirds of a freelancer's hours are billable, and once lost employer contributions and unpaid risk are added, a sustainable rate is roughly two to two and a half times salary divided by working hours.
- 70Pricing the work, and raising the priceSet a floor from your own cost arithmetic, price fixed work with 20% to 50% contingency behind a paid discovery phase, and raise the rate on new enquiries rather than waiting for permission.
- 71A pipeline, after the first three clientsA repeatable pipeline comes from a sentence somebody can forward, referrals asked for specifically, agency subcontracting and a weekly selling slot — platforms are a ramp to your first references, not a business.
- 72Writing the thing that prevents the argumentA proposal exists to make the disagreement happen in week zero, and its most valuable sections are what is excluded, what acceptance means, and how many revisions are included.
- 73Invoices, procurement, and the chaseGet onto the client's vendor system and obtain a purchase order before starting, chase on a fixed schedule rather than by mood, and keep something undelivered until the final payment clears.
- 74Registration, tax, records and insuranceMove a fixed share of every payment into a separate tax account the day it arrives, keep records weekly, and treat professional indemnity insurance as a contract requirement rather than an optional extra.
- 75Repeatable offers, retainers and the ceiling of hoursHours are capped, so income above a certain point comes from repeating a defined offer, from retainers that create a floor, or from assets you own — all of which require narrowing what you sell.
- 76The conditions under which you should take the jobFreelancing early without anyone reviewing your work produces years of uncorrected habits, and selling is a permanent 30% to 40% of the job rather than a start-up cost.
Module 9
Staying: the first ninety days and the ten years after
Getting hired is the short problem. This module covers the long one — earning trust in a new team, being the person whose numbers are right, deciding what to learn while the field shouts, refusing the thing you should not build, and knowing a plateau when you are standing on one.
By the end you can
Earn trust in a new data role within ninety days, and decide what to learn, what to refuse and when to leave using evidence rather than anxiety.
- 77The first ninety daysReproduce other people's numbers before you propose changing anything, because the mess usually encodes a reason you do not know yet.
- 78Landing in a codebase nobody documentedTrace one behaviour end to end rather than reading the codebase, and use git history to recover the reasons — the strange condition usually has a commit explaining the incident that caused it.
- 79Being trusted with a numberReputation in data work is built by catching your own mistakes before anyone else does.
- 80Which parts of this work are most exposedWell-specified work with fast verification goes first; being accountable for the answer goes last.
- 81Keeping up without drowningFollow problems and primary sources rather than launches, because almost nothing announced this month changes what you should do this month.
- 82The things you will be asked to buildYou will be asked to build something you should not; decide your line before the meeting and put the objection in writing.
- 83The second job, the plateau, and the ten-year viewA plateau is a signal to change the work, not to work harder — and the durable assets are a domain, thirty people who have seen you work, and the habit of finishing.
- 84The one-pager that gets a decisionLead with the recommendation, its cost and the decision needed by when — and attach to every number its definition, date, sample and uncertainty, because stating uncertainty is what makes a reader trust the rest.
- 85Getting promoted, and the case somebody has to makePromotion is argued for you in a room you are not in, using evidence you supplied, and it is granted for work already being done at the next level rather than as a reward for the current one.