L #2887
Applications open
Full-Time Job
AI/Ml
Quantum Gandiva AI
About the role
Job Description
Backend Developer (Python/FastAPI) – AI Agent & API Integrations
We’re looking for a backend developer to integrate AI agents with our API layer and ensure reliable, secure, and scalable data flow across the system.
What You’ll Do
- API Engineering: Design, build, and maintain RESTful services using FastAPI (or Django REST/Flask with willingness to transition).
- Agent Integration: Connect AI agents to backend services, orchestrate inputs/outputs, and standardize response handling.
- Database & Persistence: Design schemas and write efficient queries for PostgreSQL (or similar relational databases).
- Caching & Queues: Implement Redis for caching, rate-limiting, and message queuing where needed.
- Async & Background Jobs: Build background workers for long-running tasks and event-driven processing.
- Real-time Status: Implement WebSockets for live progress updates and status streams.
- File & Document Pipelines: Handle uploads, storage, retrieval, and metadata management for documents.
- Observability & Reliability: Build robust error handling, logging, monitoring hooks, and safe retry patterns.
- Documentation: Maintain clear API specs, integration notes, and internal guides.
- Testing: Write unit and integration tests to ensure stable releases and confident refactors.
What We’re Looking For
Must Have
- Strong Python fundamentals (async/await, type hints, clean architecture).
- Experience building APIs with FastAPI (or Django REST/Flask and ready to learn FastAPI quickly).
- Solid PostgreSQL / relational database experience.
- Hands-on Redis experience (caching and/or queues).
- Strong REST API design practices (versioning, validation, pagination, auth patterns).
- Comfortable with Git, code reviews, and maintainable code standards.
- Ability to read, debug, and improve existing codebases.
Nice to Have
- AWS experience (e.g., S3, ElastiCache, cloud networking basics).
- WebSocket implementation experience.
- Background processing frameworks (Celery/RQ/Arq/Temporal, etc.).
- Third-party API integrations and webhook design.
- UK property domain familiarity (a plus, not required).
What Matters Most (How We Evaluate)
- Impact > tenure: What you shipped, improved, or scaled matters more than years.
- Curiosity & craftsmanship: You’ve built something meaningful outside of tutorials.
- Debugging strength: You can trace issues across services and logs systematically.
- Learning agility: You ramp up quickly on unfamiliar systems.
- Integration mindset: You understand how APIs, databases, workers, and agents connect end-to-end.
Not a Fit If...
- You need step-by-step handholding for core backend work.
- Your experience is mostly tutorial-level with little production application.
- You struggle to work with existing codebases or explain your past work technically.
- You need step-by-step handholding for core backend work.
- Your experience is mostly tutorial-level with little production application.
- You struggle to work with existing codebases or explain your past work technically.
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If you'd like, I can also format it in a more attractive LinkedIn hiring post style (with spacing + emoji highlights) to get better reach.
Job Details
Employment: Full-time
Eligibility & process
Who can apply
Employment type: Full-time
Skills & stack
Selection process
Details are shared by the recruiter after shortlisting.
Prep kit
20 resourcesStudy material 4
Aptitude Practice Questions & Mock Tests
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
Company-Specific Interview Preparation Corner
Detailed interview experiences, exam formats, and previous test questions for Quantum Gandiva AI and tech roles.
Technical Placement Question Bank
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
Algorithm, DSA & Live Code Debugger Practice
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Company-specific guides 4
APTITUDE & LOGIC
Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
COMPANY GUIDE
Quantum Gandiva AI Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Quantum Gandiva AI and off-campus tech roles.
MOCK TESTS & PAPERS
Comprehensive Interview Prep Resources & Syllabus
Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.
CODING PRACTICE
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Python, Django, FastAPI, Flask, REST APIs, PostgreSQL, Redis, AWS) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Quantum Gandiva AI Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
Model answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer
Model answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer
Model answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer
Model answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer
Model answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Behavioural & HR questions 3
Why do you want to join Quantum Gandiva AI as a AI/Ml?
Highlight Quantum Gandiva AI's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Highlight Quantum Gandiva AI's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Competitive Package
India (Remote / Multiple), India
Employment type: Full-time
