High-Performance Software Engineer

4 days ago

Dombivli, Maharashtra, India beBeeSoftware Full time US$ 15,00,000 - US$ 20,00,000
Transforming the Future of Food

About Us:

We are a pioneering digital platform that empowers individuals to make informed, healthier food choices. Our robust network of expert nutritionists guides users on a tailored journey to eating well while saving time and money.

We seamlessly integrate dietary assessments and personalized meal planning with online food ordering, optimizing ingredients for both home and on-the-go needs. We partner with national retailers across the U.S., many of whom accept SNAP/EBT, making nutritious food more accessible.

Our mission is to revolutionize the way people access healthy food, regardless of economic status. We are driven by a set of core values that shape our culture and work environment:

  • **Data-Driven**: We make informed decisions based on data.
  • **Impactful**: We drive real-world change through our solutions.
  • **Collaborative**: We support each other's growth and development.
  • **Innovative**: We maintain a healthy appetite for learning and improvement.
  • **Joyful**: We take pride in our work and celebrate each other's successes.
About the Role:

Our AI Engineering team is at the forefront of transforming how millions access personalized nutrition and care. The Staff AI Engineer will play a critical role in designing and deploying cutting-edge solutions that drive real-world impact—like tailoring communications, improving engagement, and reducing missed appointments.

  • Develop high-performance backend applications using TypeScript, Node.js, and relational databases like PostgreSQL or MySQL.
  • Build and maintain APIs and microservices that enable real-time and batch inference of AI/ML models.
  • Fine-tune and adapt pre-trained models for specific business use cases.
  • Integrate external AI services where appropriate.
  • Collaborate with cross-functional teams to deliver end-to-end AI-powered features.
  • Contribute to MLOps workflows, including deployment, versioning, and monitoring of models in production.
  • Follow engineering best practices for clean code, testing, observability, and reliability.
  • Stay up to date with advancements in AI tooling and infrastructure to continuously improve system performance and model impact.

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