Senior Full-Stack Engineer — AI/ML

EduBridge Learning; Mumbai; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

EduBridge Learning is seeking a Full-Stack Engineer to take complete ownership of its intern-developed product portfolio. This role demands transitioning products from fragile prototypes to production-grade systems within 90 days while embedding AI/ML capabilities for intelligent automation.

A successful candidate will have 5+ years of full-stack engineering experience, strong Python and AWS skills, and be capable of mentoring a growing team of developers.

Qualifications

• 5+ years of full-stack engineering experience, including AI/ML deployment experience.
• Strong competence in Python (Django or FastAPI) and frontend frameworks (React preferred).
• Experience with AWS and production databases essential.

Responsibilities

• Take complete ownership of intern-developed product portfolio.
• Drive AI/ML layer across products for intelligent automation.
• Mentor interns and junior developers as the team grows.

Skills

Full-stack engineering

Python (Django/FastAPI)

React (or equivalent modern framework)

AWS (EC2, S3, RDS, Lambda)

AI/ML deployment

Tools

PostgreSQL

CI/CD tools

MLflow

Job description

Full‑Stack Engineering

Context

Why This Role Exists

EduBridge Group runs three business lines

EduBridge, BridgeBeyond, and TalentDeploy scaling toward 150 Cr revenue by FY

The Product & Learning Solutions team is the group capability supplier : we build the platforms, integrations, and intelligent systems that make learning and delivery possible at scale.

Several of our internal products currently built and run by an intern team have crossed proof‑of‑concept. They are useful, but they are fragile, undocumented, and dependent on individuals who will move on. They now need a senior owner who can harden them for production and evolve them into AI‑powered platforms that scale.

This role is created for that owner.

The Mandate

You will take complete end‑to‑end ownership of the intern‑built product portfolio from Day 1 including code, architecture, infrastructure, deployment, roadmap, and stakeholders. Within 90 days, these products must transition from "fragile prototypes" to "production‑grade, observable, documented, scalable systems".

Beyond stabilization, you will drive the AI/ML layer across our products embedding intelligent automation, personalization, content generation, and predictive analytics where they create measurable business value.

What You'll Own

Product Ownership (End‑to‑End)

• Take complete handover of all intern‑developed products: code, infra, integrations, documentation
• Establish source control, CI/CD, testing standards, and deployment discipline
• Build proper architecture documentation, runbooks, and observability
• Own the 12‑month product roadmap in partnership with business stakeholders

Full‑Stack Engineering

Backend

• Python (Django / FastAPI), REST APIs, microservices, async processing

Frontend

• React (or equivalent modern framework), responsive UI

Data

• PostgreSQL / MySQL / MongoDB; data modeling and migrations

Cloud & DevOps

• AWS (EC2, S3, RDS, Lambda)
• CI/CD pipelines

Integrations

• Internal platforms (ELITE LMS, Do-Select, partner APIs, NSDC-SIDH, Tata Tele) and third‑party APIs

AI/ML Engineering

• Design, train and deploy ML models for learner personalization, content recommendation, automated assessment, retention prediction, document/OCR automation
• Ship LLM‑powered features in production (RAG, agentic workflows, content/assessment generation) using leading commercial and open‑source models
• Build evaluation pipelines, guardrails, and cost‑monitoring for AI features – this is non‑negotiable
• Translate emerging AI capability into concrete business application

Cross‑Functional Leadership

• Single point of accountability for these products in business reviews
• Translate problems from L&D, Service Excellence, PPV, and HR into product features
• Mentor interns and junior developers as the team grows under you

Must‑Have

• 58 years of full‑stack engineering experience, with at least 2 years deploying AI/ML in production (not just notebooks)
• Strong Python (Django or FastAPI) + frontend competence (React preferred)
• Hands‑on AWS experience and production database expertise
• Demonstrated experience with LLMs in production, RAG, prompt engineering, model orchestration, evaluation
• Track record of taking over and stabilizing legacy or poorly‑documented codebases (this is critical for the first 90 days)
• Strong written communication – architecture docs, decision logs, technical specs

Good‑to‑Have

• EdTech, HRTech, or workforce‑development domain experience
• Familiarity with LMS, SCORM/xAPI, assessment engines
• MLOps tooling (MLflow, SageMaker, Vertex AI)
• Vector databases (pgvector, Pinecone, Weaviate)
• Indian language NLP / regional speech processing experience