Backend Developer at Locus.sh · Bengaluru (Bangalore) · 4 - 8 years · ₹35L - ₹50L / yr (ESOP available) · Posted 30 Nov 2021

Locus is a global decision- making platform in the supply chain that uses deep learning and proprietary algorithms to provide route optimization, real-time tracking, insights and analytics, beat optimization, efficient warehouse management, vehicle allocation and utilization, intuitive 3D packing and measurement of packages. Locus automates human decisions required to transport a package or a person, between any two points on earth, delivering gains along efficiency, consistency, and transparency in operations.
Locus, which has achieved a peak of 1 million orders processed in a day (200,000 orders an hour) and is trained & tested on over 100 million+ order deliveries, works in 75 cities across the globe. Locus works with several large-scale market leaders like Urban Ladder, Tata Group of Companies, Droplet, Licious, Rollick, Lenskart, other global FMCG, pharma, e-commerce, 3PL and logistics conglomerates.
Locus is backed by some of the biggest names in the market and recently raised $22 MN in Series B funding and also $4 Mn in a pre-Series B round. Earlier, In 2016, Locus raised $2.75 Mn (INR 18.3 Cr) in a Series A funding.
Locus was started by Nishith Rastogi and Geet Garg, two ex-Amazon engineers on a mission to democratize logistics intelligence for businesses across industries. Nishith was profiled by Forbes Asia in their ’30 Under 30’ 2018 list. Geet, on the other hand, holds a dual degree (BTech and MTech) in Computer Science and Engineering from the Indian Institute of Technology. Our team constitutes of engineers from Indian Institute of Technology and Birla Institute of Technology & Science- Pilani, and Data Scientists with PhDs from Carnegie Mellon University and Tata Institute of Fundamental Research. Our multifaced product and business team is led by senior members from Barclays, Google & Goldman Sachs with immense operational execution experience.
Job Description
- Design & implement backend APIs at Locus.sh
- Mentor junior developers technically.
- Actively work to reduce tech debt in the Locus backend
- Work towards more stability & scalability of the backend
- Tech stack - Java, AWS, Aurora etc.
Eligibility
- 4-8 years of product company experience
- OOP implementation experience. Programming language does not matter, We use Java internally but have hired folks from non Java background.
- Hands on experience in SQL, Dynamo DB, Postgres etc preferred.
- Prior experience building REST APIs
- Advanced understanding of AWS stack
- Prior knowledge of solving problems at scale.
Perks:
- Healthy catered meals at office
- You decide your own Work From Home (WFH) and Out Of Office (OOO)
- Pet-friendly - bring your pets to the office any day. Locus family already has a Rottweiler and a Beagle

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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 safe, 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 modern 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.
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