Senior Backend Engineer

COGNIFLIX ARTIFICIAL INTELLIGENCE PVT LTD (MINDFLIX AI)

Senior Backend Engineer / Lead Engineer Bengaluru · Full-Time · 6 to 8 Years· INR 30L to 40+LPA

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 / Lead Engineer (6 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.

Location Bengaluru - On-site

Employment Full-Time

Experience 6- 8 years of production backend engineering / lead

Compensation INR 30L 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 30–45 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.