Estimated breakdown of entry-level AI roles by type (based on job posting analysis, Lightcast / LinkedIn, 2025-26)
The three tracks
AI Application / Wrapper (42%)
Build products using LLM APIs (OpenAI, Anthropic, Gemini). Integrate AI into existing software. Ship AI-powered features. Skills: Python, API integration, prompt engineering, basic backend. Does NOT require ML theory.
AI Analyst / BI (31%)
Use AI tools to analyse data and produce insights. Build AI-assisted dashboards. Write queries, interpret model outputs. Skills: SQL, Python, Tableau/BI, statistics. ML knowledge helpful but not required.
Core ML / Model Building (17%)
Train, fine-tune, or evaluate models. Requires solid ML theory, PyTorch/TensorFlow, linear algebra and calculus. PhD or strong research background increasingly expected at top firms.
MLOps / Infrastructure (10%)
Deploy models to production, build pipelines, monitor drift. Requires DevOps + ML knowledge: Docker, Kubernetes, CI/CD, cloud platforms. Often overlooked but well-paid and less competitive.
Which track suits you
I code but have no ML background
Application/Wrapper track. Learn Python + API integrations + prompt engineering. Ship something in 4 weeks.
I work with data but not code heavily
AI Analyst track. SQL + Python + a BI tool. AI is changing this role fast and demand is high.
I have a CS/stats background and want to go deep
Core ML track. PyTorch + linear algebra + a strong GitHub is the starting point. Entry is harder but ceiling is highest.
I like systems and infrastructure
MLOps track. DevOps skills + ML awareness. Extremely in demand and undersupplied at entry level.
The single biggest mistake students make is treating "AI jobs" as one category. A prompt engineer at a Series B startup and an ML research engineer at Google DeepMind require almost entirely different preparation. The Application and Analyst tracks are significantly more accessible at entry level and represent nearly three quarters of all postings. If you are starting from scratch, these are the tracks with the shortest path from zero to hired.
Source: Burning Glass Technologies AI skills demand report 2025, LinkedIn Jobs AI category analysis Q4 2025, World Economic Forum Future of Jobs Report 2025, OECD AI in the labour market 2025
Approximate skill frequency in entry-level AI job postings (based on job posting analysis, Stack Overflow / LinkedIn / Lightcast, 2025-26)
Python
Required in the vast majority of AI postings. Depth matters: not just scripts. Data manipulation with Pandas, working with APIs, writing clean, readable functions. The bare minimum is 3 months of daily coding practice.
SQL
Cited across most data-adjacent AI postings. Joins, aggregations, window functions, subqueries. Most data work in AI roles happens upstream of any model. If you cannot query data, you cannot do the job.
Git
Expected in nearly all technical AI postings. Branches, pull requests, commit messages. A GitHub profile with consistent green squares is visible evidence of practice. An empty GitHub is a red flag to most technical interviewers.
What "knowing Python" actually means to a hiring manager
Many students list Python on their resume after completing one online course. Interviewers test this. The threshold is: can you write a script from scratch to clean a dataset, call an API, handle errors, and output a structured file - without Googling the basic syntax. If you need to look up how to open a file or write a for loop, you are not at the required level. The Pandas + requests + JSON trio is the practical entry point. Get to where you can build something small from a blank file in under an hour.
Core ML / Research track additions
PyTorch (preferred over TensorFlow)
Leads TensorFlow in ML postings (37.7% vs 32.9% of framework-specific listings; 85% of deep learning research papers). PyTorch dominates research and most production ML.
Linear algebra + calculus basics
Not tested directly but essential for understanding models. 3Blue1Brown Essence series is the fastest path.
Statistics and probability
Frequently required in data-adjacent AI roles. Distributions, hypothesis testing, Bayes theorem, confidence intervals.
A Kaggle competition (top 20%)
Public evidence of applied ML. More credible than any course certificate.
Application / Wrapper track additions
OpenAI / Anthropic / Gemini API
Build things that use LLM outputs. Chain prompts. Handle rate limits and errors. This is the hands-on minimum.
LangChain or LlamaIndex basics
Frameworks for building LLM applications. Widely used in production app development at startups.
Basic backend (FastAPI or Flask)
To ship your AI feature as a product, not just a notebook. Essential for deployed projects.
Prompt engineering
System prompts, few-shot examples, chain-of-thought. Practical skill, not a soft concept.
Source: Burning Glass Technologies AI skills demand 2025, LinkedIn Economic Graph AI Skills Report Q3 2025, Stack Overflow Developer Survey 2025, JetBrains State of Developer Ecosystem 2025
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Entry-level AI role salary ranges (US, USD annual): low to high based on Glassdoor, levels.fyi, LinkedIn Salary 2025-26
$85-130k
MLOps Engineer entry-level range (US). True entry-level starts ~$85K; range reflects early-career with adjacent DevOps or ML experience. High demand, undersupplied talent pool.
$92-135k
ML Engineer (entry-level, US). Glassdoor median $108k. Big Tech pays significantly above this band.
$68-98k
AI Data Analyst (entry-level, US). Most accessible entry point. Median $79k across sectors (LinkedIn Salary 2025).
Global equivalents for entry-level ML Engineer roles
United States - $92,000 to $135,000/yr
ML Engineer entry-level (Glassdoor median $108K). AI PM entry-level is $85K-$120K. MLOps starts ~$85K. San Francisco Bay Area at the top. NYC and Seattle close.
United Kingdom - £45,000 to £72,000/yr
London (DeepMind, Wayve, Stability AI, Meta AI). Manchester and Edinburgh emerging. Big Tech London competes with Bay Area on equity.
Germany - EUR 52,000 to EUR 78,000/yr
Berlin (Delivery Hero, Zalando AI), Munich (BMW AI, Allianz tech). Strong for European AI startups.
India - INR 5 to 12 LPA (fresher)
Bengaluru dominates. Fresher/entry-level national range INR 5-10 LPA. MNCs (Google, Microsoft, Amazon) reach INR 18-24 LPA for strong candidates. Funded startups INR 10-15 LPA.
Singapore - SGD $65,000 to $105,000/yr
Regional AI hub. GovTech, Sea Group, Grab, Shopee all hiring. Government AIAP programme for fresh graduates.
Australia - AUD $70,000 to $115,000/yr
Sydney (Atlassian, Canva AI) and Melbourne lead. PayScale shows ~AUD $69K for under 1 year; broader market average ~AUD $98K. Government AI roles through ADHA and ASD.
Source: Glassdoor ML Engineer salary data 2025, levels.fyi entry-level ML data Q4 2025, LinkedIn Salary AI roles 2025, PayScale India ML Engineer 2025, Glassdoor UK AI roles 2025, Singapore GovTech AIAP programme documentation
Where domain knowledge wins
Healthcare / BioTech AI
Clinical trial optimisation, drug discovery, diagnostic imaging. Biology or medicine background plus Python beats pure CS at most medtech companies.
Finance / FinTech AI
Risk models, fraud detection, trading algorithms, credit scoring. Finance + ML is a rare and very well-paid combination. Every bank's quant team needs this profile.
Legal AI / LegalTech
Contract analysis, case prediction, document review. Law background plus LLM API skills is a nearly uncrowded entry point in 2026.
Climate / Energy AI
Grid optimisation, emissions modelling, satellite analysis. Engineering + AI is the hiring profile for this sector.
Where pure ML wins
Foundation model labs
Anthropic, OpenAI, Google DeepMind, Meta AI. Pure research. PhD expected or exceptional undergraduate research output.
AI infrastructure companies
Hugging Face, Scale AI, Cohere, Mistral. ML engineering is the core product. Strong ML fundamentals required.
Autonomous systems
Self-driving (Waymo, Cruise), robotics (Boston Dynamics, Figure). Strong control theory + ML background.
Recommendation / ranking systems
Meta, Netflix, TikTok, Spotify. Very competitive. Strong stats + systems background plus significant internship experience expected.
"The best AI candidates we hire are not the ones who know the most ML theory. They are the ones who understand the problem well enough to know when to use a model and when not to."
This quote, from a VP of Engineering at a Series C fintech, reflects a pattern visible across hiring data. At companies using AI as a means to an end (the majority of AI hiring), domain-fluent candidates who can code outcompete pure ML candidates who cannot communicate business value. If you have a non-CS background, your fastest path into AI is not to replicate a CS degree. It is to add Python, SQL, and working knowledge of 2 to 3 ML techniques on top of the domain knowledge you already have. That combination is rarer and often more valuable than a general ML skill set with no sector context.
Source: LinkedIn AI Hiring Trends Report 2025, Deloitte AI in the enterprise survey 2025, McKinsey Global AI Survey 2025, World Economic Forum AI talent gap analysis 2025
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Portfolio signal strength (hiring manager survey weight, 1-10): higher is stronger
9.2/10
Deployed project with live URL. Highest-weighted signal. Shows you can ship, not just experiment in a notebook.
4.2/10
Certificate (Coursera, Google, etc). Lowest-weighted signal. Demonstrates you completed a course, not that you can do the job.
8.5/10
Open source contribution to a used library. Shows code quality, collaboration, and ability to work in a real codebase.
What a strong AI portfolio looks like in practice
The deployed project (highest priority)
An AI-powered tool with a live URL. Does not need to be sophisticated: a sentiment analyser on product reviews, a document Q+A bot built with LangChain, a price prediction model with a simple front end. The key is that someone other than you can use it. Hosted on Hugging Face Spaces, Vercel, or Streamlit Cloud (all free).
The Kaggle proof
Compete in a Kaggle competition and land in the top 20% of submissions. Write a detailed notebook explaining your approach. This is more credible than any course because it involves real competition. Medal-level performance (top 10%) is a strong signal even at senior levels.
The GitHub commit history
Hiring managers look at commit frequency and recency. A profile with consistent commits over 6+ months signals genuine practice. Cold-starting GitHub 2 weeks before applying is obvious. One strong, well-documented repository is worth more than 10 sparse ones.
The technical writeup
A blog post or Substack article where you explain how you built something, what did not work, and why you made each technical decision. Demonstrates communication ability, which is the second most-cited hiring criterion after technical skill.
Certs (use selectively)
Coursera / Google AI / deeplearning.ai certs are useful as a learning scaffold, not as a hiring signal. List them if they are from recognised names (Andrew Ng's courses, AWS ML Specialty, GCP Professional ML Engineer) but do not rely on them. A cert without a project is empty.
Source: Triplebyte AI hiring signals report 2024, Towards Data Science hiring manager survey 2025, LinkedIn AI recruiter interviews Q1 2026, Kaggle annual survey on ML in industry 2025
Relative AI job posting density by city (SF Bay Area = 100): entry-level and junior roles
San Francisco Bay AreaOpenAI, Anthropic, Google DeepMind, Meta AI, Scale AI, Cohere. Highest density. Also highest cost of living - $3,000+/month for a room.
Index 100
SeattleAmazon Web Services AI, Microsoft Azure AI, Waymo. Strong for MLOps and cloud-native AI. Lower cost than SF.
Index 54
New York CityFinance AI (Goldman, JPMorgan, Two Sigma), media AI, LegalTech. Best city for domain-specific AI roles in finance and law.
Index 48
Bengaluru, IndiaGoogle India, Microsoft India, Walmart Global Tech, PhonePe, Swiggy AI teams. Fastest-growing AI market by absolute volume.
Index 22
SingaporeSea Group, GovTech AIAP, Grab, regional AI labs. English-speaking gateway for Southeast Asia AI roles.
Index 20
LondonDeepMind, Wayve, Stability AI, Magic Pony. Smaller but growing. Best European market for research-adjacent AI.
Index 18
The remote layer is real - for the right roles
Around 27% of entry-level AI postings specifically offered full or hybrid remote in 2025 (vs ~34% for AI roles broadly across all seniority levels). This skews heavily toward Application/Wrapper and Analyst roles. Core ML research roles at Big Tech remain almost entirely in-person. If you are targeting an AI startup at the application layer, location is a significantly smaller barrier than it was 3 years ago. A strong portfolio and a solid GitHub profile can land you a remote role at a US company from India, Eastern Europe, or Southeast Asia.
Source: LinkedIn Jobs AI category geographic analysis Q4 2025, Burning Glass AI regional hiring data 2025, Glassdoor remote AI jobs tracker 2025, Indeed AI job trends report 2025
Job boards and platforms
LinkedIn Jobs
Largest volume. Use filters: Entry Level + Machine Learning / AI. Set alerts. Apply within 48 hours of posting.
Wellfound (Wellfound.com)
Best for startup AI roles. Salary ranges shown upfront. Founder-posted roles often here 1-2 weeks before LinkedIn.
Hugging Face Jobs
AI-specific. Mostly ML engineering and research. Small volume but very high signal quality.
Y Combinator job board
YC companies post here. Strong for early-stage AI startup roles. roles.y-combinator.com
Studojo Internship Dojo
Curated AI internships globally. Pay data, sector filters, direct applications.
Off-board channels (higher conversion)
AI researcher Twitter/X
Follow researchers at labs you want to join. Intern and junior role announcements often appear here before job boards. Reply with a relevant project.
Discord servers
Hugging Face Discord, LangChain Discord, local AI community servers. Founders and hiring managers are active. Direct conversations happen here.
GitHub sponsors and contributors
Find repos in your target domain. Contribute meaningfully. Maintainers hire contributors they already know.
Cold email with a project
A 3-line email to a relevant team lead, with a link to something you built that is relevant to their product, converts at 3-8%. One of the highest-ROI channels at entry level.
Source: Wellfound 2025 startup hiring report, Y Combinator AI company hiring data, LinkedIn Talent Solutions AI hiring guide 2025, Lattice AI hiring channels survey 2025
90-day preparation sprint (Application track: assumes basic Python familiarity)
Days 1-30: Build the floor
Python: Pandas + requests + JSON. Build 3 small scripts daily. Target: reading and writing code fluently without googling syntax.
SQL: Complete Mode Analytics SQL tutorial (free). Write 50 queries. Cover joins, aggregations, window functions.
Git: Learn branching, commits, pull requests. Make your first open source PR (even docs fixes count).
LLM basics: Call the OpenAI API. Build a simple chat interface. Understand tokens, temperature, and system prompts.
Days 31-60: Build something real
Pick one project: a document Q+A bot, a sentiment tool for product reviews, an AI writing assistant for a specific domain.
Use LangChain or the OpenAI API directly. Add a simple FastAPI backend. Deploy to Hugging Face Spaces or Streamlit Cloud.
Write a technical post explaining what you built, what did not work, and what you learned.
Start a Kaggle competition in parallel. Focus on EDA and a clean baseline submission first.
Days 61-90: Apply and iterate
Polish your GitHub: clear READMEs, commit history visible, pinned repos pointing to your best work.
Update your resume: lead with the deployed project and skills. Remove anything that is not relevant to AI.
Apply to 5 to 10 roles per week. Target: startups on Wellfound, internships on Studojo, YC companies.
For each rejection or silence: improve one concrete thing. A rejected portfolio is not a failure. It is a data point.
What to do if you have a non-CS background
The 90-day path above assumes you can already write basic Python. If you are starting from zero, add 30 days at the front for Python fundamentals (CS50P is free and excellent). If you have a non-CS background in a domain with strong AI adoption (medicine, law, finance, engineering), prioritise learning AI within your domain rather than pivoting to a generic CS profile. A medical student who can build a symptom-checker prototype and explain its limitations is more hireable at a healthtech company than a CS student who cannot name a clinical workflow.
Source: Towards Data Science community survey 2025, fast.ai "Practical Deep Learning" course data, deeplearning.ai learner outcomes report 2025, Andrej Karpathy "Software 2.0" framework, Kaggle Learn guided paths
Work on things that matter.
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