AI/ML Engineer

AI ML Engineer

Must-Have Skills (2-4 years experience required)

• 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, and 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, and 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 the 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 ModelsDesign 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. Productization & 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 the 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: In person