Full Stack Engineer

Role: Full Stack Engineer

• Location: Mumbai (Hybrid)
• Expereince: 2 – 5 Years (flexible based on quality of talent)
• Budget: 20 - 25 LPA
• Notice Period: 30 days or less preferred
  

Notes From Hiring Manager

• Candidates from reputed Product-based companies are preferred.
• Quick Commerce (QC) domain experience is good to have.
• Strong hands-on experience with distributed systems, caching, and event-driven architectures (Kafka, RabbitMQ).
• Exposure to Big Data pipelines (Spark, Flink) and building interactive analytics dashboards.
• Familiarity with ML model deployment and MLOps workflows.
  

About The Company

ultra-fast commerce vertical

10 minutes or less

cutting-edge technology, advanced data science, AI-driven forecasting, and last-mile logistics optimization

About the role:-

Full Stack Engineers (with a backend focus)

Responsibilities:-

• Architect, design, and deliver highly scalable backend systems, APIs, and data pipelines for an AI-driven SaaS platform.
• Build responsive, intuitive front-end interfaces and dashboards that make complex supply chain insights easy to act on.
• Ensure platform robustness, security, and high availability to support real-time decision-making.
• Work cross-functionally with product managers, data scientists, and engineers to translate business requirements into elegant technical solutions.
• Drive engineering best practices (CI/CD, testing, code reviews, observability) to maintain high-quality, reliable software.
  

Qualifications:-

• B.Tech/B.Eng in Computer Science or IT from a reputed university.
• 2+ years of experience in full stack development with backend-heavy expertise.
• Strong backend experience: REST APIs, Python, Django, Docker, PostgreSQL/RDS, MongoDB, Redis, AWS/GCP.
• Frontend skills: React.js, Redux, HTML, CSS, JavaScript, Material UI (or similar), charting libraries (Highcharts/D3/Recharts).
• Solid understanding of OOP, Data Structures & Algorithms, and system scalability.
• Experience solving system design and data scalability challenges.
• Analytical and data-driven approach to problem solving.
  

Preferred Qualifications:-

• Experience with distributed systems, caching, and event-driven architectures (Kafka, RabbitMQ).
• Exposure to Big Data pipelines (Spark, Flink) and interactive analytics dashboards.
• Familiarity with ML model deployment and MLOps workflows.
• Previous experience in SaaS, supply chain tech, quick commerce, or logistics platforms.