Epitomsoft Academy · Module 6 of 6
Your career in the age of advanced AI
Turn what you have learned into a job: AI-assisted software engineering, emerging roles, a portfolio that stands out, and interview preparation.
What you will learn
From graduate to AI-ready professional
Employers increasingly expect every engineer to work effectively with AI – and new roles are appearing fast. This module helps you choose a direction, build proof of your skills and present them with confidence.
- Use AI coding assistants productively – and responsibly
- Understand emerging roles and the skills each requires
- Build a portfolio project that demonstrates real AI engineering
- Write a résumé and LinkedIn profile that highlight impact
- Prepare for technical and behavioural interviews
Module at a glance
- Format: live online sessions + hands-on lab
- Level: All levels
- Prerequisites: Modules 1–5 (or equivalent experience)
- You will build: An Epitomsoft portfolio project, résumé and mock interview
Topics
Key concepts, explained with examples
AI-assisted software engineering
AI assistants can draft code, tests and documentation – but you remain responsible for correctness, security and design. Review everything.
Example: You ask an assistant to write unit tests for a payment function, then add the edge cases it missed (negative amounts, currency rounding).
Emerging AI roles
AI engineer, LLMOps/MLOps engineer, AI product manager, AI quality and evaluation engineer, AI security specialist and data engineer for AI.
Example: An “AI quality engineer” designs eval suites and red-team tests – a natural next step for QA professionals.
The skills employers ask for
Solid programming (Python plus a typed language), APIs, cloud basics, data handling, RAG and agents, evaluation, security awareness and communication.
Example: A typical job post: “Python, REST APIs, vector databases, LLM evaluation, AWS or Azure, strong written communication.”
A portfolio that stands out
One complete, well-documented project beats ten tutorials. Show the problem, your design, evaluation results and lessons learned.
Example: “Campus Help Assistant: RAG over 300 handbook pages, 92% accuracy on a 50-question eval, deployed with a public demo and README.”
Résumé bullets that show impact
Use action + what you built + measurable result. Mention tools only where they support the story.
Example: “Built a RAG assistant (Python, pgvector) that answered 92% of 50 test questions correctly, reducing manual lookups in a student-services pilot.”
Interview preparation
Expect coding, system design (“design a document Q&A system”), AI concepts (hallucinations, RAG vs. fine-tuning) and behavioural questions.
Example: Practise explaining trade-offs: “I chose RAG over fine-tuning because the policies change monthly and answers need citations.”
Your first 90 days on the job
Learn the codebase and the business, deliver a small win early, and share what you learn.
Example: Days 1–30: learn and fix small bugs. Days 31–60: own a feature. Days 61–90: propose an AI improvement with measured impact.
Lifelong learning
AI changes monthly. Build habits: follow primary sources, read release notes, contribute to open source and teach others.
Example: Spend one hour a week reproducing a new open-source project and writing a short summary on LinkedIn.
Worked example
Epitomsoft project ideas
Pick one, build it end-to-end, and make it the centrepiece of your portfolio.
RAG
Campus or company knowledge assistant
Answers questions over a public handbook with citations, evaluation results and a simple web interface.
Agents
AI test-case generator
Reads user stories, proposes test cases, generates automated tests and reports coverage – reviewed by a human.
Data
Natural-language analytics
Lets users ask questions of a public dataset in plain English and returns charts with the generated SQL shown.
Security
Prompt-injection detector
Flags suspicious instructions hidden in emails or web pages before an AI assistant processes them.
Productivity
Meeting-notes to action items
Turns transcripts into decisions, owners and due dates in structured JSON, with an accuracy evaluation.
Social good
Accessibility helper
Generates alt text and plain-language summaries for public documents, evaluated with real users.
Hands-on lab
Career sprint
A guided sprint that ends with you ready to apply – with one-to-one coaching from our engineers.
- Choose your target role and map your skill gaps
- Build and publish your Epitomsoft project on GitHub with a strong README
- Rewrite your résumé and LinkedIn profile around impact
- Complete two mock technical interviews and one behavioural interview
- Create a 30-60-90 day plan for your first role
Key terms
Vocabulary to master
- AI engineer
- LLMOps
- MLOps
- AI product manager
- Eval engineer
- AI security
- Portfolio
- README
- System design
- Behavioural interview
- STAR method
- 30-60-90 plan
- Open-source contribution
Check your understanding
Module 6 quiz
Score 70% or more to pass. Pass all six module quizzes to unlock your Frontier AI certificate on My progress.
Further learning
Free & open-source references
Original research, open-source projects and free courses – all free to access.
- AI Engineer roadmap – free, community-maintained learning roadmap
- Hugging Face Learn – free courses on LLMs, agents and more
- Practical Deep Learning for Coders (fast.ai) – free, hands-on deep learning course
- Kaggle Learn – free micro-courses with real datasets
- Google Machine Learning Crash Course – free ML fundamentals course
- GitHub Skills – free interactive courses for Git, GitHub and Actions
- freeCodeCamp – free curriculum and certifications
- Generative AI for Beginners (GitHub) – free, open-source course from Microsoft
Ready to master frontier AI?
Join the Epitomsoft Academy advanced programme – tell us about yourself and we will be in touch.