Senior Backend Engineer

COGNIFLIX ARTIFICIAL INTELLIGENCE PVT LTD (MINDFLIX AI)

Senior Backend Engineer

Bengaluru Full-Time 4 to 8 Years INR 20L to 40LPA

ABOUT MINDFLIX AI

Cogniflix Artificial Intelligence Pvt Ltd (https://www.mindflix.ai/) is building the next generation of AI-powered personalisation and engagement systems across voice, conversational interfaces, and real-time intelligence. Our platform combines Generative AI, deep learning, and scalable backend infrastructure to enable human-like interactions at production scale.

We are a focused engineering team working on problems at the intersection of distributed systems, real-time communication, and applied AI. The work is technically demanding and the stakes are real our systems handle live conversations, time-critical data pipelines, and multi-tenant workloads in production.

ROLE OVERVIEW

We are hiring a Senior Backend Engineer (4 to 8 years) to own core infrastructure across our backend services, real-time systems, and AI integration layer. This is a hands-on engineering role, you will design, build, and operate systems that run in production, not prototypes.

You will work alongside a small, senior team and report directly into engineering leadership. The expectation is deep ownership: you define the solution, ship it, monitor it, and improve it.

ROLE AT A GLANCE

Role

Senior Backend Engineer

Location

Bengaluru - On-site / Hybrid

Employment

Full-Time

Experience

4- 8 years of production backend engineering

Compensation

INR 22L to 40 LPA (based on experience)

KEY RESPONSIBILITIES

Backend Services & APIs

Design, build, and ship production-grade backend services and REST APIs in Python

Own service reliability fault-tolerant request handling, graceful degradation, and clear error contracts

Build and maintain background processing pipelines: job scheduling, retry logic, idempotency, and dead-letter handling

Manage multi-tenant data models with correct isolation, schema design, and access control

Real-Time & Distributed Systems

Build and operate WebSocket-based real-time systems that run correctly across multiple instances behind a load balancer

Solve distributed state management problems: session handling, in-memory lifecycle, and cross-instance cleanup without shared state dependencies

Design background processors that coordinate correctly no race conditions, no silent data loss, no infinite retry loops

Implement and tune connection pooling, caching strategies, and memory management for high-throughput workloads

AI & Integration Layer

Implement retrieval and context orchestration patterns RAG, memory management, tool/function calling in production services

Build stateful conversation and session management systems with correct lifecycle semantics

Integrate third-party APIs (voice, telephony, AI vendors) behind clean abstraction layers that survive vendor changes

Implement evaluation frameworks, regression test sets, and quality scoring for LLM-powered services

Instrument services for production observability: structured logging, metrics, distributed tracing, and alerting

Ownership & Quality

Write code that is readable, testable, and maintainable

Treat 'shipped' as the beginning of the job, not the end monitor, measure, and continuously improve

Participate actively in design reviews and raise quality issues early

Write runbooks, incident post-mortems, and internal technical documentation

REQUIRED SKILLS & BACKGROUND

Must Have

4 - 8 years of production backend engineering experience

Strong Python clean code, performance-conscious, async-capable

PostgreSQL at depth: query optimisation, indexing, migrations, connection pooling

Distributed systems: multi-instance deployments, ALB/load balancer behaviour, session state, race conditions

AWS hands-on: EC2, Lambda, managed services, CloudWatch

WebSocket expertise connection lifecycle, session management, multi-instance correctness

Background job systems: retry logic, idempotency, scheduling semantics

Memory management and profiling in Python you have found and fixed leaks in production

REST API design versioned, well-documented, properly tested

CI/CD and Git-based engineering workflow

Good to Have

Experience with voice or telephony API integrations

LLM integration: prompt engineering, context composition, streaming, function calling

RAG pipelines and vector search (PgVector, Pinecone, Weaviate, or equivalent)

Multi-tenant SaaS architecture: row-level security, tenant isolation patterns

Observability tooling: Sentry, Datadog, OpenTelemetry, or equivalent

System design for scalable AI services stateless services, orchestration layers

Frameworks: LangChain / LangGraph / LlamaIndex or equivalent

Redis for distributed caching or pub-sub

Docker, container basics, and infrastructure-as-code fundamentals

Startup or early-stage engineering experience

WHAT WE ARE LOOKING FOR

The engineering values we hire for

Strong fundamentals. You understand why systems behave the way they do not just how to make them work today.

Ownership mindset. Build test monitor improve. You don't hand off and forget.

Pragmatic problem-solving. You find the right solution for the constraints not the most elegant one in isolation.

Comfort with ambiguity. Requirements at this stage are starting points. You ask the right questions, make reasonable assumptions, and move forward.

Production-first thinking. You think about failure modes, edge cases, and observability before you write the code.

Clear communication. Small team. You flag blockers early, document decisions, and don't let things go dark.

TECHNOLOGY ENVIRONMENT

Backend

Python FastAPI SQLAlchemy

AI / ML

Large Language Models Retrieval-Augmented Generation Embeddings TTS Integration

Infrastructure

AWS (EC2, Lambda, ALB, Elastic Beanstalk, RDS, S3) PostgreSQL with vector extensions

Real-Time

WebSockets Event-driven background processing

Observability

CloudWatch Structured logging Alerting pipelines

Workflow

GitHub CI/CD Code review culture

WHAT WE OFFER

Competitive compensation commensurate with experience

Direct access to the founding team. Your work is visible and your decisions carry weight

Technically challenging problems in distributed systems, real-time infrastructure, and applied AI

A small, high-ownership team where engineering quality is taken seriously

Hardware of your choice and tooling budget

INTERVIEW PROCESS

01

Screening Call

A focused 3045 minute conversation with engineering leadership. We want to understand the systems you have built, the problems you have solved, and how you think about backend engineering. Come prepared to discuss specific technical decisions you have made in previous roles.

02

Technical Assessment

A take-home assessment grounded in real engineering scenarios distributed systems, backend design, and production reasoning. We assess depth of understanding and quality of thinking, not speed. Maximum 3 hours. Submissions are reviewed carefully before the next round.

03

Founder Round

A direct conversation with the Founder & CEO. This covers engineering philosophy, how you work in early-stage environments, and what you are looking for at this stage of your career. This is also your opportunity to assess whether Mindflix is the right place for you.

HOW TO APPLY

Send the following to careers@mindflix.ai / abhinav@mindflix.ai with the subject line:

Senior Backend Engineer - Direct - [Your Name]

Your resume and LinkedIn profile

A brief note (one paragraph) describing a production backend system you built what the problem was, what you built, and what you would do differently today

Links to any public work (GitHub, technical writing, open-source contributions) if available optional but valued

We review every application and respond within 5 business days. One follow-up is always welcome if you have not heard back.