FULLTIME
Machine Learning Engineer AI Mentor
KS
keystone software llc
Not specified · onsite · Posted 44d ago
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Section · 01
About this role
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
What we're looking for
Nice to have
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
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
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.
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
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
Nice to have
- Experience with education or behavioral data
- Familiarity with LLMs, RAG and guardrails
- MLOps and cloud deployment experience
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
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
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.
Sourced from foundit
Section · 02
Skills
evaluation driftCloud DeploymentMLOpsEthical AiML FundamentalsPythonmodern ML tooling