Job Description

Must-Have Skills

5+ years in software development, with 2-3+ years in AI/ML / data-heavy products / ad-tech / mar-tech / e-commerce tools. Strong backend skills in Python (FastAPI/Django/Flask) or Node.js/TypeScript. Practical ML experience: scikit-learn, XGBoost, or deep learning frameworks (PyTorch/TensorFlow). Comfortable working with large datasets, feature engineering, model evaluation. Experience with 3rd party APIs, ideally: Amazon SP-API / Advertising API, or other marketplace/ad APIs. Strong knowledge of SQL and relational databases (PostgreSQL/MySQL). Good understanding of cloud platforms (AWS/GCP/Azure), Docker, task queues (Celery/Resque/RQ, etc.). Ability to own a project end-to-end: architecture ? implementation ? deployment ? iteration.
Key Responsibilities

1. Product & Architecture (Helium 10-style tool)

Design overall system architecture for an AI-powered SaaS tool for: Product & keyword research Competitor tracking Ads & campaign optimization Listing quality & ranking insights Build a scalable, modular backend so we can plug in more marketplaces and ad channels over time. Decide on tech stack, data storage, and cloud architecture (with founder).
2. API Integrations (Amazon + Ads + Analytics)

Integrate with platforms such as: Amazon SP-API / Advertising API Google Ads, Meta Ads, other ad platforms (later) Analytics tools if required Build data ingestion services to: Sync products, keywords, campaigns, orders, and performance data Normalise and join data across platforms Handle OAuth, tokens, refresh logic, and rate limits Create reusable connectors so new marketplaces/APIs can be added quickly.
3. AI / Machine Learning Models

Design and implement ML/AI models for: Performance forecasting & campaign duration planning Keyword harvesting / keyword recommendations Budget & bid optimization suggestions Audience/placement insights Anomaly detection (sudden drop in ROAS, spike in ACoS, etc.) Experiment with different approaches: classic ML, time-series forecasting, clustering, and (where relevant) LLM-based analysis. Continuously improve models using real campaign data and feedback from marketers.
4. Data Analysis & Visualisation

Build dashboards and visualizations for: Performance by campaign / ad group / keyword Cross-channel view (Google + Meta + Amazon etc.) Lifetime value, ROAS, TACoS, ACOS, profitability, etc. Work with UX/UI or front-end devs to make insights simple, visual, and actionable for non-technical users.
5. Productisation & SaaS

Turn models and analytics into SaaS features: "Recommendations" widgets (e.g., "Pause these 3 keywords", "Increase budget here") Automated rules / workflows (e.g., trigger alerts or changes based on conditions) Contribute to multi-tenant architecture, billing logic, roles & access, and usage logging. Collaborate with the team on roadmap, feature prioritization, and beta testing with real clients.
6. Quality, Security & Documentation

Write clean, maintainable, well-tested code. Implement basic MLOps practices: model versioning, monitoring, and performance tracking. Maintain clear technical documentation for APIs, data schemas, and models. Follow best practices for data privacy and security, especially around client ad accounts.7. SaaS & Multi-tenant Platform Build a secure multi-tenant SaaS: User management, roles & permissions Subscription plans, usage limits Billing integration (Stripe/Razorpay/etc.) Implement logging, monitoring, and error tracking to keep the system stable.
Job Type: Full-time

Pay: ₹40,000.00 - ₹80,000.00 per month

Work Location: Remote