Senior Full-Stack Engineer — AI/ML

Keylabsintelli; Mumbai; On-site; INR 1,800,000 - 2,500,000; Full time

Job summary

Keylabsintelli is looking for a Senior Product Owner in Mumbai, responsible for transitioning intern-developed products into stable, production-grade systems. You will lead AI integration and drive the product roadmap in collaboration with stakeholders, ensuring scalable and observable architectures.

The ideal candidate should have 5 to 8 years of engineering experience, including AI/ML deployment, and strong skills in Python and AWS.

Qualifications

• 5–8 years of full-stack engineering experience, with at least 2 years deploying AI/ML in production.
• Strong Python and frontend competence.
• Hands-on AWS experience and production database expertise.

Responsibilities

• Own the intern-built product portfolio from Day 1.
• Transition products from prototypes to production-grade systems.
• Drive AI/ML layer across products, embedding automation and analytics.

Skills

Python (Django or FastAPI)

Frontend competence (React preferred)

AWS experience

Production database expertise

LLMs in production

Communication skills

Tools

PostgreSQL

MongoDB

MLflow

SageMaker

Vertex AI

Job description

EduBridge Group runs three business lines — EduBridge, BridgeBeyond, and TalentDeploy — scaling toward ₹150 Cr revenue by FY28. 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.

2. The Mandate

You will take complete end‑to‑end ownership of the intern‑built product portfolio from Day 1 — 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.

3. What You'll Own

A. 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
• Frontend: React (or equivalent modern framework), responsive UI
• Data: PostgreSQL / MySQL / MongoDB; data modeling and migrations
• Cloud & DevOps: AWS (we run on 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
• 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

D. 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

4. Must‑Have

• 5–8 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

5. Good‑to‑Have

• 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