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 4+ years building and deploying ML in production

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 Perks 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 level4+ years 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. Growth opportunities 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 (Agasteeswaram)
🏢 KeyStone Software
📍 Agasteeswaram