Must-Have Skills
- 2+ years in software development, with 1-2+ 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 Responsibilities1. Product & Architecture (Helium 10style 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.
