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DECIPHERU · COURSE

AI Career Transition

An 8-week Applied AI foundation course for software engineers, data scientists, and adjacent professionals transitioning into AI engineering, ML engineering, AI product, and applied research roles. Cybersecurity convergence covered throughout.

Designed by Julian Calvo, Ed.D.

Founder, DecipherU. Cybersecurity career educator.

8 modules32 lessons55 hours of video and exercisesSelf-paced
$397One-time payment
  • 14-day refund. No questions asked.
  • Lifetime access on all enrolled cybersecurity courses
  • Newly published

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What you will learn

  • Name the four primary Applied AI role families and the day-to-day work of each
  • Write STAR-method resume bullets that quantify the situation, task, action, and result for AI-relevant work
  • Apply the four-axis project selection rubric (signal, ship-ability, depth, defensibility) to candidate projects
  • Build the project against the week 3 brief on the two-week timeline
  • Author LinkedIn outreach messages that produce reply rates above 25 percent
  • Run an AI system design interview against the recurring 2026 patterns (RAG, agents, evaluation, cost reduction)

Curriculum

  1. Module 1 · 360 minFree preview

    Mapping Your AI Transition Target

    The Applied AI roles, the four named target roles (AI engineer, ML engineer, AI product, applied researcher) and their adjacencies, current skill stack inventory, gap analysis against the target, and a written transition target document the learner returns to in every later module.

    Learning objectives

    • Name the four primary Applied AI role families and the day-to-day work of each
    • Inventory your current skill stack against a public O*NET-style framework so gaps are explicit, not anecdotal
    • Identify the two to three target roles where your current background offers the strongest signal
    • Produce a one-page transition target document that anchors every later week's artifact

    Lessons

    • The Applied AI role picture in 2026Preview
    • Inventorying your current skill stack
    • Gap analysis against the target role
    • Authoring the transition target document

  2. Module 2 · 360 min

    Resume Tuning for AI Roles

    STAR-method bullet writing for AI engineer, ML engineer, AI product, and applied researcher resumes; AI keyword targeting that survives ATS screening without keyword stuffing; format choices that work for both human reviewers and machine parsers; the resume artifact the learner ships at the end of week 2.

    Learning objectives

    • Write STAR-method resume bullets that quantify the situation, task, action, and result for AI-relevant work
    • Target AI keywords from real job descriptions without triggering keyword-stuffing filters
    • Choose a resume format that survives ATS parsing and human review
    • Ship a one to two page AI-tuned resume at the end of week 2

    Lessons

    • STAR-method bullets for AI work
    • AI keyword targeting without stuffing
    • Format choices for ATS and human reviewers
    • Shipping the resume artifact

  3. Module 3 · 360 min

    Portfolio Projects Part 1: Project Selection

    How to pick portfolio projects that signal AI capability to a hiring panel; the four-axis selection rubric (signal, ship-ability, depth, defensibility); ten archetype projects mapped to the four target role families; the written project brief the learner ships at the end of week 3.

    Learning objectives

    • Apply the four-axis project selection rubric (signal, ship-ability, depth, defensibility) to candidate projects
    • Pick a project that maps to the target role family from week 1 with strong signal-to-effort ratio
    • Write a one-page project brief that scopes the project to fit a two-week build window
    • Identify the named failure modes of weak portfolio projects (toy demos, replicas, undefendable claims)

    Lessons

    • The four-axis project selection rubric
    • Ten archetype projects mapped to target roles
    • Failure modes of weak portfolio projects
    • Authoring the project brief

  4. Module 4 · 540 min

    Portfolio Projects Part 2: Shipping

    Building the project against the week 3 brief; documentation that survives an interview review; hosting on a public surface (GitHub, Hugging Face Spaces, Vercel) so the hiring panel can see the work; the shipped artifact at the end of week 4.

    Learning objectives

    • Build the project against the week 3 brief on the two-week timeline
    • Author a README that survives an interview review (design rationale, evaluation methodology, results, limitations)
    • Host the project on a public surface so the hiring panel can read the code and run the demo
    • Produce a 5-minute Loom or written walk-through that the hiring panel can review before the interview

    Lessons

    • Building against the brief
    • README that survives an interview review
    • Hosting on a public surface
    • Recording the walk-through

  5. Module 5 · 360 min

    Network Building Plan

    LinkedIn outreach patterns that produce calls without spam, conference and meetup attendance for the target role family, community engagement on the platforms the AI hiring community uses (Twitter/X, Hacker News, AI conferences), and the network plan the learner runs across weeks 5 through 8.

    Learning objectives

    • Author LinkedIn outreach messages that produce reply rates above 25 percent
    • Identify the conferences and meetups where the target role family hires from
    • Engage the platforms where the AI hiring community reads (Twitter/X, Hacker News, conference Slacks)
    • Run a four-week network plan with named contact targets and named outcomes

    Lessons

    • LinkedIn outreach that produces calls
    • Conferences and meetups for the target role
    • Engaging the platforms the AI hiring community reads
    • The four-week network plan

  6. Module 6 · 540 min

    AI Engineering Interview Preparation

    AI-specific system design (LLM API integration, evaluation, latency-cost-quality trade-offs); behavioral interview patterns for AI engineers and ML engineers; technical interview format expectations across frontier labs, AI-native enterprises, and traditional companies adopting AI; the interview prep evidence the learner produces in week 6.

    Learning objectives

    • Run an AI system design interview against the recurring 2026 patterns (RAG, agents, evaluation, cost reduction)
    • Author behavioral STAR stories that hold up under AI hiring panel pressure
    • Identify the three named hiring patterns (frontier lab, AI-native enterprise, traditional company adopting AI) and prepare for each
    • Run two mock interviews and produce a written self-assessment of strengths and gaps

    Lessons

    • AI system design interview patterns
    • Behavioral interview STAR stories for AI roles
    • Three hiring patterns: frontier lab, AI-native enterprise, traditional
    • The week 6 mock interview lab

  7. Module 7 · 360 min

    AI Compensation Patterns

    Base salary, equity, bonus, and total compensation patterns across frontier AI labs, AI-native enterprises, and traditional companies adopting AI; how to read offer structures including refreshers and acceleration; the negotiation script that adds 10 to 25 percent on a typical offer; the offer-comparison spreadsheet template the learner builds in week 7.

    Learning objectives

    • Read AI compensation offers across the three hiring buckets and identify the components that drive total comp
    • Compare offers honestly across base, equity, refresh, vesting schedule, and bonus
    • Run a negotiation conversation that adds 10 to 25 percent without breaking the offer
    • Build an offer-comparison spreadsheet that supports the actual decision

    Lessons

    • Base, equity, bonus across the three buckets
    • Reading offer structures: refreshers, acceleration, vesting
    • Negotiation script for AI offers
    • Building the offer-comparison spreadsheet

  8. Module 8 · 360 min

    Capstone: 90-Day Transition Plan

    Integrating the inventory, gap analysis, resume, portfolio, network plan, interview prep, and compensation work into a single 90-day transition plan with named commitments, weekly outcomes, and the public accountability structure the learner runs.

    Learning objectives

    • Integrate the seven prior week deliverables into a single 90-day transition plan
    • Set named weekly outcome commitments across application volume, network volume, and interview volume
    • Build the public accountability structure that holds the plan together (peer accountability, weekly review, written log)
    • Author the capstone document the candidate runs against for the next 90 days

    Lessons

    • Integrating the seven prior deliverables
    • Setting weekly outcome commitments
    • Building the public accountability structure
    • Authoring the capstone 90-day plan document

Who this is for

Software engineers (3 to 10 years experience) targeting AI engineering or ML engineering roles

Software engineers (3 to 10 years experience) targeting AI engineering or ML engineering roles

Data scientists targeting senior AI engineering or applied research roles

Data scientists targeting senior AI engineering or applied research roles

Platform engineers and SREs targeting AI infrastructure or ML platform roles

Platform engineers and SREs targeting AI infrastructure or ML platform roles

Prerequisites

Required

  • At least 3 years of professional software engineering, data science, or adjacent technical experience
  • Working familiarity with at least one production codebase (review pull requests, ship features end to end)
  • Comfort with at least one programming language used in AI work (Python, TypeScript, Go, or Rust)
  • Basic understanding of LLMs as a user (prompting, API calls, structured output) is helpful but not required
  • Willingness to commit 50 to 60 hours of focused study and artifact production across 8 weeks

Recommended

  • Familiarity with general cybersecurity terminology
  • Time to commit roughly 4 to 6 hours per week

Instructor

Julian Calvo, Ed.D.

Founder and CEO, DecipherU

Julian Calvo, Ed.D., is an applied learning scientist and founder of DecipherU. He holds an Ed.D. in Learning Sciences and an MBA in Marketing, and is completing his M.S. in Applied AI specializing in Cybersecurity at Northeastern University. He writes on cybersecurity career design, the AI-cybersecurity convergence, adult learning, and the gap between what security teams say they need and what they actually select for.

Why I built this cybersecurity course: every module answers a question I was asked by a real career changer, paired with a primary-source citation that supports the answer.

Frequently asked questions

Who is this cybersecurity course for?
Career changers and working cybersecurity professionals who want practical, primary-source-grounded instruction. Each lesson connects an industry standard to a real workplace task and ends with a knowledge check.
How long does this cybersecurity course take?
Self-paced. Most learners finish in 4 to 8 weeks at 4 to 6 hours per week. Lifetime access means you can move at any cadence that fits your schedule.
Does the cybersecurity course include a certificate?
Yes. A verifiable certificate of completion is issued automatically when you finish the final module. The certificate has a public verification URL anyone can use to confirm authenticity.
Can I get a refund?
Yes. You have 14 days from purchase to request a full refund, no questions asked. Email support@decipheru.com with your order number and we will process the refund within 3 business days.
Will this prepare me for a specific cybersecurity certification?
DecipherU courses build the conceptual scaffolding behind major cybersecurity certifications. Pair this course with the official training resource from the certifying body for exam-specific coverage.
Is technical experience required?
Each course states explicit prerequisites in the Prerequisites section above. Most foundations courses assume basic IT literacy and command-line familiarity, not deep cybersecurity experience.
How is the cybersecurity course delivered?
Video lessons paired with written instruction, knowledge checks, and module quizzes. Notes auto-save to your account so you can pick up where you left off across devices.
Who built this cybersecurity course?
Julian Calvo, Ed.D., founder of DecipherU. Every course is grounded in peer-reviewed research, NIST and MITRE primary sources, and BLS occupational data with explicit citations.
Last verified: April 2026?Report an inaccuracy

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Course content is for educational purposes only and does not constitute professional advice. All claims are supported by cited peer-reviewed academic research. DecipherU does not teach or reproduce any proprietary sales methodology. Verify all referenced sources independently.