About The Role

What you'll be doing

You will develop and ship the predictive models behind AI Mentor — scoring learner risk in real time and powering automated interventions across email, SMS and the LMS. Practical, grounded ML with measurable impact on student success.

Key responsibilities

Your day to day

Build, evaluate and deploy learner-risk and engagement models
Design features from LMS, CRM and portal signals
Stand up monitoring, evaluation and retraining pipelines
Partner with engineering to ship models into production

Requirements

What we're looking for

Strong ML fundamentals and hands-on projects — freshers welcome
Strong Python and modern ML tooling
Solid grasp of evaluation, drift and responsible AI
Ability to translate fuzzy goals into shippable models

Preferred Qualifications

Nice to have

Experience with education or behavioral data
Familiarity with LLMs, RAG and guardrails
MLOps and cloud deployment experience

Benefits

What we offer

Competitive salary, reviewed yearly

Fully remote with flexible hours

Generous learning & development budget

Paid time off and wellbeing support

Top-tier equipment of your choice

Global, inclusive engineering culture

Learning opportunities

How you'll grow

How predictive retention works at real institutional scale
Responsible AI practice in a sensitive domain
End-to-end ownership from model to measurable outcome

FAQ

Questions, answered

Is this role fully remote

Yes — we are remote-first and hire globally. You can work from wherever you do your best work, with overlap hours for collaboration.

What does the hiring process look like

Application, a practical assessment, assessment review, an HR discussion, a technical interview, and a final decision — typically wrapped up in 2–3 weeks.

Do I need education-technology experience

It helps, but it is not required. We value strong engineering and a genuine interest in education; we will get you up to speed on the domain.

Who will I report to

You will report to the Head of AI, and work day to day inside a small, senior pod.

DepartmentAI

LocationOn-site

Nagercoil

Employment typeFull-time

Experience levelFreshers only

Working modelOn-site

Reports toHead of AI

Estimated hiring timeline

Around 2–3 weeks from application to offer.

The team

A small, senior AI pod — engineers, product and QA who own outcomes together.

Hiring process

From application to offer

01

Application submission

Submit your details and resume through our application form.

02

Assessment platform

Complete a practical assessment or short course assigned to you.

03

Assessment review

Our team reviews your submission and shortlists candidates.

04

HR discussion

A Conversation About You, Your Goals, And The Role.

05

Technical interview

A practical session on real ed-tech problems — no whiteboard gotchas.

06

Final selection

A fast, transparent offer built around how you do your best work.

Why join ZionStar

More than a job.

Career growth

Clear progression, real ownership, and the room to grow into the engineer or leader you want to be.

Mentorship

Work alongside senior ed-tech engineers who review your code and invest in your craft.

Real-world projects

Ship software used by real universities, EdTech companies and hundreds of thousands of learners.

Global exposure

Collaborate across continents on projects for organizations worldwide.

Learning opportunities

A generous learning budget and time to use it — we build ed-tech, so we keep learning too.

Engineering culture

Clean architecture, tested code, small senior pods, and weekly shipping. No bureaucracy.

Ready to apply

Tell us about yourself. After you submit, you'll complete a short assessment so we can get to know your work.

Machine Learning Engineer AI Mentor