AI Platform
/Individual contributor at the frontier
AI Infrastructure & MLOps Engineer
Design, deploy, and scale the AI infrastructure powering EduRankAI's intelligent applications and machine learning platforms.
Compensation
₹22,00,000 – ₹45,00,000 per annum
Engagement
Full-Time
Permanent role. Full-time commitment. This is an on-site role at Kolkata. It is not remote and not hybrid.
Scope of role
Own complex problems end-to-end. Set technical direction. Mentor mid and junior teammates.
01 — The role
Why this role exists at EduRankAI
EduRankAI is building an Educational Intelligence Ecosystem powered by Artificial Intelligence, Large Language Models, intelligent agents, decision intelligence, and advanced computational technologies. We are seeking a Senior AI Infrastructure & MLOps Engineer to design, deploy, optimize, and scale the infrastructure that powers model training, inference, experimentation, monitoring, and production AI systems. You will build reliable, secure, and scalable platforms that enable AI researchers and engineers to innovate faster.
02 — The work
What you will own
- 01 Design scalable infrastructure for AI workloads.
- 02 Deploy and manage production model serving platforms.
- 03 Optimize GPU utilization and inference performance.
- 04 Build distributed training and inference systems.
- 05 Design and automate MLOps pipelines.
- 06 Implement model versioning and experiment tracking.
- 07 Develop CI/CD pipelines for AI applications.
- 08 Build automated model retraining workflows.
- 09 Deploy and manage production AI applications.
- 10 Monitor model performance and operational health.
- 11 Implement deployment, rollback, and release strategies.
- 12 Develop AI observability and monitoring solutions.
- 13 Manage vector database and feature store infrastructure.
- 14 Collaborate with AI Engineers, Data Scientists, DevOps, and Product teams.
- 15 Continuously improve platform reliability, scalability, and automation.
03 — The expertise
What we look for
04 — The bar
Who thrives here
- → Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Engineering, Software Engineering, or a related field.
- → 5–10+ years of experience in AI Infrastructure, MLOps, Cloud Engineering, Platform Engineering, or DevOps.
- → Experience deploying and managing production machine learning systems.
- → Strong understanding of distributed systems, cloud infrastructure, and container orchestration.
- → Experience with Kubernetes, Docker, MLflow, Kubeflow, Ray, or Apache Airflow.
- → Experience deploying Large Language Models and AI inference services.
- → Knowledge of vector databases, feature stores, and retrieval systems.
- → Experience building CI/CD pipelines and infrastructure automation.
- → Proficiency in Python and modern backend development practices.
- → Experience with AWS, Microsoft Azure, Google Cloud Platform, or equivalent cloud platforms.
- → Strong troubleshooting, performance optimization, and system reliability skills.
- → Ability to collaborate effectively with AI Engineers, Researchers, DevOps Engineers, and Product teams.
- → Passion for building scalable, reliable, and production-ready AI infrastructure.
Terms of Engagement
How working time works at this level
No counted hours. A five-day week with a four-hour overlap window so colleagues can reach you, and objectives agreed for the cycle.
Type
Senior
Working days
5 per week
Rest days
2 per week
Hours counted
No
Daily overlap
4h
Measured by
Objectives agreed for the cycle, the quality of the judgement calls, and the people the work makes better. Not attendance.
Where this stands legally
Hours are not counted at this level. The statutory ceiling of 9 hours a day and 48 a week remains the limit the role is designed to fit inside: if the work cannot be done within a normal week, the scope is wrong, not the person.
The full per-level model is published at Working Hours by Level.
05 — Hiring process
What to expect after you apply
- 01
Application review
Every application is read personally within five business days. We respond either way.
- 02
Take-home or live exercise
Role-specific. Time-boxed. Real problems we are actually working on, not invented puzzles.
- 03
Conversations
Deep technical and values conversations with the team you would join. No trick questions. No panel ambushes.
- 04
Offer or honest no
If yes: digital offer letter, signed in-portal, transparent terms. If no: written feedback if you want it.
Before you start
What we will collect. What it costs. What we will not do with it.
We will collect
- Name, email, phone — Account + application updates. No marketing.
- Resume / portfolio link — Human review of your work.
- Date + place of birth — Identity verification only.
- Your written responses — Selection rubric. Read by humans.
- Government ID (later) — Anti-fraud at offer / interview stage. Not at signup.
We will never
- Sell your data
- Share with third-party recruiters
- Use for advertising
- Train models on it
- Send marketing email
Our situation
EduRankAI is a small, independent organization building long-term capabilities in educational intelligence, advanced AI systems, and research infrastructure. We take no advertiser money, no donations with strings attached, and no investor pressure on hiring decisions. Applying is free, and every application is read by a human — recruitment, technical, academic and leadership teams. It buys us the right to be honest.
Ready to apply?
We read every application personally. If you are the right person for this role — regardless of pedigree, background, or where you are based — you will hear back from us within five business days.
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