Senior Backend Engineer

Brahma AI operates at the intersection of enterprise Media Asset Management (MAM) and cutting-edge generative media. We build and scale industry-leading generative AI models, including hyper-realistic digital humans (ATMAN) and multilingual voice synthesis (VAANI), for world-class enterprise clients in entertainment, sports, healthcare, and retail.
We are looking for a Senior Backend Engineer to design and scale the core server-side services, APIs, and data pipelines that power our AI and media processing capabilities.
In this role, you will focus primarily on backend architecture, API design, and system performance. You will collaborate closely with ML engineers to integrate generative models and with dedicated DevOps engineers who handle our platform infrastructure. You will also touch frontend interfaces occasionally to help expose backend capabilities seamlessly.
Key Responsibilities
API & System Architecture: Design, build, and maintain high-performance RESTful APIs, microservices, and event-driven architectures for AI-powered media workflows.
Data Processing & Storage: Build robust data pipelines, database schemas, and caching layers (PostgreSQL, Redis) for handling large-scale media content.
ML Model Integration: Collaborate with ML researchers and engineers to integrate model inference and training workflows into production backend systems.
Operational Health & Observability: Implement application-level logging, metrics, and alerting; actively participate in performance profiling, root-cause analysis, and production incident resolution alongside our DevOps team.
Performance Optimisation: Optimise server-side code, async queues, and database queries for low latency, high throughput, and high availability.
Cross-Functional Collaboration: Partner with frontend/product teams to ensure API contracts make complex generative AI capabilities intuitive to consume.
Must Haves
5+ years of software engineering experience, with primary expertise in server-side Python development.
System Design & APIs: Proven track record building scalable RESTful APIs, microservices, and async/event-driven systems.
Data & Caching: Strong relational database design (e.g., PostgreSQL) and caching/memory store experience (e.g., Redis).
Operational Mindset: Experience with production observability (logging, monitoring, alerting) and a disciplined approach to debugging, testing, and incident response.
AI coding Literacy: Pragmatic experience using AI development tools (e.g., Copilot, Cursor, LLM APIs) to boost coding velocity while maintaining strict code quality, test coverage, and architectural rigor.
AI concept awareness: Understanding of core AI concepts (e.g., the operational differences, latency requirements, and resource profiles of model training vs. inference).
Fullstack Awareness: Rudimentary knowledge of modern JavaScript/TypeScript and web development concepts—enough to collaborate seamlessly with UI engineers or build internal prototypes.
Within +/- 3 hours from GMT.
Nice to Have
ML Experience: Hands-on experience or familiarity with PyTorch for model implementations, as well as ML serving tools (e.g., TorchServe, vLLM, Triton).
Data Pipelines & Storage Formats: Practical experience with DAG-based workflow orchestrators (e.g., Airflow, Prefect, Temporal), async ETL processing, and working with columnar data formats like Parquet.
Media & Computer Vision: Experience with media processing tools and computer vision libraries (e.g., FFmpeg, OpenCV, WebGL/Canvas).
Advanced Storage & Messaging: Familiarity with graph databases (Neo4j) or distributed messaging/event streaming systems (Kafka, RabbitMQ).
DevOps Collaboration: Basic exposure to cloud services (AWS/GCP) and container standards (Docker) to collaborate seamlessly with our platform team.
Domain Background: Background in media technology, graphics programming, VFX, or fast-paced engineering startups.