Interviews
How to introduce yourself in an AI interview: building a 2-minute pitch
Positioning, specialisation, one representative project and what you are looking for: a structure you can use as-is before your next interview.
12 min read
Career hub
AI Careers
Read the market. Build the right skills. Grow your career in AI.
Roles, skills, finding an opportunity, interviews, professional profile and freelancing: the essentials for AI professionals.
Worth reading this week
A short selection, picked for professional usefulness.
Interviews
Presenting an AI project without reciting your stack
Problem, constraints, choices, trade-offs, evaluation, production, impact: the structure that shows your real level instead of a tool list.
August 26, 2026 · 11 min read
Market & roles
AI Engineer, ML Engineer, Data Engineer: how the roles are being redrawn
The titles look alike, the expectations don't. Scopes, overlaps and the signals that place a role before you apply.
August 25, 2026 · 10 min read
Skills & watch
MCP: is it now a useful skill for an AI Engineer?
What this standard actually changes, which profiles it matters for, how deep to go, and how to discuss it in interviews without hype.
August 24, 2026 · 9 min read
The AI market right now
The signals worth following for your career.
- Rising skill
Evaluation is becoming a skill of its own
Being able to measure the quality of an AI system, not only to make it work, comes up earlier and earlier in hiring conversations.
- Emerging role
Positions centred on agents and orchestration
Titles remain unstable, but the scope is sharpening: tools, memory, guardrails and the reliability of chained steps.
- Company expectations
Production weighs more than the prototype
Organisations past the exploration phase look for people who can run, monitor and evolve an existing system.
- In-demand technology
Inference cost control enters the criteria
Model choice, caching, quality-cost trade-offs: these topics now show up in technical interviews.
- Freelance / permanent
Short engagements to frame, longer roles to industrialise
Freelancing often serves the framing and kick-off stage; internal roles follow once the system has to be maintained over time.
A qualitative editorial reading, with no invented figures: these signals will later be supported by technology watch and aggregated trends from engagements posted on Talent AI.
Where AI careers lead
The role families found today in organisations deploying AI.
AI Engineer
Building model-based applications: integration, evaluation, production.
ML Engineer
Training, industrialising and keeping models reliable inside systems.
Data Scientist
Modelling, experimentation and a statistical reading of business problems.
AI-oriented Data Engineer
Pipelines, quality and availability of the data feeding AI systems.
MLOps
Deployment, monitoring, inference cost and model lifecycle.
GenAI Engineer
Generative applications: RAG, agents, tools, guardrails and evaluation.
AI Product
Framing use cases, product trade-offs, measuring value and risk.
AI Architect
Overall architecture, technology choices, security and compliance.
AI Solutions
Bridging client needs and technical feasibility, prototyping and rollout.
RAG / Agents specialist
Retrieval, tool orchestration, memory and answer quality.
This map is indicative: titles vary between companies and keep evolving.
Practical advice
Concrete pointers you can apply to your next application or interview.
Building a two-minute pitch
Three beats: your current specialisation, one representative project with its context and main constraint, then the kind of problem you want to work on next. Not a full career timeline.
Read the full guidePresenting an AI project without reciting the stack
Start with the problem and the constraint (latency, cost, quality, data), then the trade-offs you took and rejected. Technologies illustrate the story; they are never the subject.
Read the full guideWhen you don't know the answer
Say so, then reason out loud: what you do know about the area, how you would verify it, which adjacent experience you would draw on. Structured reasoning beats a confident approximation.
Latest articles
The most recent publications in this hub.
Skills & watch
AI agents: what you should actually be able to do before discussing them in interviews
Orchestration, tools, memory, guardrails, evaluation. A frame to tell a demo apart from something a team can run in production.
Skills & watchTalent AI analysis
RAG: is it still a differentiating skill for an AI professional?
RAG became routine at prototype stage. What is valued now: indexing quality, evaluation and cost control.
Finding an opportunity
Reading an AI job ad: spotting the real scope behind the title
Team maturity, ownership of data, production expectations. The signals that tell you whether the role fits your trajectory.
Interviews
AI system design interviews: structuring your answer in six steps
Framing, data, model, evaluation, production, cost. A structure that holds up under pressure.
Finding an opportunity
Targeted applications: why five considered ones beat thirty sent out
Adapt your positioning to the team's context, make explicit what you are there to solve, and make the decision easy on the company side.
Profile & personal branding
Portfolio and GitHub: proving AI expertise without piling up projects
Two well-documented projects beat ten unfinished repositories. What a technical reviewer looks at first.
Profile & personal branding
Making your profile readable for companies hiring in AI
Specialisation, project context, level of responsibility: what lets a company place you immediately.
Skills & watchTalent AI analysis
MLOps: a side skill or a career path of its own?
Industrialisation, monitoring, inference cost. Why this skill changes the nature of AI roles beyond the prototype.
Market & rolesMarket
GenAI Engineer, AI Product, AI Architect: roles still being defined
These titles cover very different realities depending on company maturity. How to decode them before committing.
Career & freelance
Going freelance in AI: the decisions to settle before you start
Positioning, type of engagements, pace, quiet periods. A decision frame, with no invented numbers.
Career & freelance
Negotiating an AI engagement: discuss value before price
Scope, deliverables, responsibility, duration. What gets negotiated before the rate — and usually sets it.
Market & rolesTalent AI analysis
From data science to applied AI: which skills transfer first
What carries over directly, what requires real learning, and the realistic intermediate steps.
Skills to watch
The topics that come up most often in hiring conversations.
Select a skill to filter the articles and tips.
AI Careers is not a news feed
AI News tracks the ecosystem: models, companies, research, products. AI Careers explains what those shifts actually change for skills, roles, hiring and careers.
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