Role overview

The client is building a multimodal AI platform that processes multi-hour video, audio and text to generate structured insights, narratives and highlight workflows for broadcasters and media organisations.

We are seeking a Backend / Platform Engineer to design and build high-throughput media pipelines, robust APIs, and model-serving infrastructure that connect our AI engine (video perception + multimodal reasoning) to real products and customer environments.

This is not a CRUD‑only backend role.

You will work on:

- long‑running jobs
- distributed processing
- GPU inference orchestration
- storage for embeddings and metadata
- integration with AI models
- reliability and observability at scale

Key responsibilities

Media ingestion & processing pipelines

- Design and implement ingestion pipelines for multi‑hour video and audio content.
- Build microservices for frame extraction, audio processing, transcription integration and metadata generation.
- Handle long‑running, asynchronous jobs using queues, workers and robust retry strategies.
- Integrate with FFmpeg or similar tools for transcoding, segmenting and preparing media for AI models.

API & platform architecture

- Design and implement REST/gRPC APIs that expose AI model outputs (perception, multimodal alignment, narratives) to frontend and external systems.
- Define clear contracts for internal services and external integrations.
- Implement authentication, authorisation and rate‑limiting for platform endpoints.
- Ensure backward‑compatible API evolution as the product matures.

Model‑serving & AI integration

- Integrate with AI inference services (video models, multimodal models, LLM/VLM) running on GPUs or specialised infrastructure.
- Design request/response flows that handle large payloads, streaming outputs and structured results.
- Optimise throughput and latency for inference pipelines, including batching, caching and concurrency control.
- Collaborate closely with AI engineers to productionise models and debug end‑to‑end behaviour.

Storage,



data models & performance

- Design data models to store embeddings, timelines, metadata, scene/shot boundaries, and narrative units.
- Work with appropriate storage technologies (SQL/NoSQL, object storage, search indices) based on access patterns.
- Implement indexing and query strategies for fast retrieval of segments, highlights and multimodal insights.
- Optimise performance for large datasets and high‑volume workloads.

Reliability, observability & operations

- Implement logging, metrics and tracing across services for debugging and monitoring.
- Set up health checks, circuit breakers and graceful degradation for critical services.
- Work with CI/CD pipelines to ensure protected, repeatable deployments.
- Collaborate on Kubernetes‑based deployments (or equivalent orchestration) for scaling services.

Requirements (must‑have)

Experience:

- 4–8 years in backend or platform engineering.
- At least 3 years working on distributed systems, high‑throughput services or complex pipelines (not just simple CRUD apps).

Languages & frameworks:

- Strong proficiency in Python or Node.js (one primary, both are a plus).
- Experience with at least one contemporary backend framework (FastAPI, Flask, Express, NestJS, etc.).

Distributed systems & pipelines:

- Hands‑on experience with queues and workers (e.g. Celery, RabbitMQ, Kafka, SQS, etc.).
- Experience building asynchronous, long‑running job pipelines.
- Understanding of idempotency, retries, backoff, and failure handling.

APIs & integration:

- Strong experience designing and implementing REST APIs (gRPC is a plus).
- Experience integrating with external services and handling network‑level failures.





Cloud & infrastructure:

- Experience deploying services on AWS, GCP or Azure (EC2/Compute Engine, S3/GCS, IAM, networking basics).
- Experience with Docker; exposure to Kubernetes is a strong plus.

Data & storage:

- Experience with SQL and at least one NoSQL store.
- Ability to design schemas and data models for performance and maintainability.

Engineering quality:

- Strong debugging skills across services and environments.
- Experience with unit/integration tests for backend systems.
- Clear, structured communication in English.

Nice‑to‑have

- Experience with media/video processing (FFmpeg, transcoding, segmenting).
- Experience with AI/ML model integration (serving models, handling inference requests).
- Experience with search/retrieval systems (e.g. Elasticsearch, vector databases).
- Experience with observability stacks (Prometheus, Grafana, OpenTelemetry).
- Experience working with remote teams across time zones.

What we are explicitly NOT looking for

To reduce noise and mismatches, we are not looking for:

- Pure CRUD‑only backend developers with no pipeline or distributed systems experience.
- Engineers who have only worked on small, single‑service apps without scale or complexity.
- Candidates who cannot explain trade‑offs in architecture, data modelling and reliability.
- Candidates who are uncomfortable with ownership of subsystems end‑to‑end.

Why join us

- Work on real, complex problems at the intersection of media, AI and distributed systems.
- Collaborate with senior AI engineers working on perception, multimodal fusion and narrative reasoning.
- Build the core platform that turns AI models into a usable product for broadcasters and media organisations.
- Operate with high ownership, clear expectations and direct access to the CTO.

Skills:- Python, NodeJS (Node.js), RESTful APIs, Distributed Systems, Amazon Web Services (AWS), FFmpeg, RabbitMQ, SQL, MongoDB, NOSQL Databases, Video processing and Audio processing

📌 Backend Engineer (India)
🏢 Cutshort Lightning
📍 India