AI-ML Engineer

1 day ago

Jaipur, Rajasthan, India HRL Infotechs Full time
  • Must-Have Skills (2-4 years)
  • 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
  • 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 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).
    1. 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.
    1. 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.

    1. Data Analysis & VisualisationBuild 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.
    1. Productisation & SaaSTurn 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.
    1. Quality, Security & DocumentationWrite 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.
    1. SaaS & Multi-tenant PlatformBuild 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.

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