Senior Platform Engineer - Distributed Systems

Description :

We're looking for a Senior Platform Engineer to architect and build the robust infrastructure that powers our next-generation Voice AI platform. In this role, you'll design and implement systems that handle massive scale, ensure ultra-low latency, and deliver exceptional reliability. This is a high-impact role where your work will form the technical backbone of real-time AI conversations used by some of India's leading enterprises.

Responsibilities :

- End-to-end ownership : Drive product development from requirements gathering to architecture, implementation, deployment, and post-launch support.

- Scale massively : Design and build infrastructure capable of supporting hundreds of thousands of concurrent AI conversations daily.

- Innovate constantly : Stay on top of the latest research and technology to implement cutting-edge infrastructure solutions.

- Architect for growth : Design highly scalable, distributed systems that adapt to evolving business needs and product features.

- Optimise relentlessly : Identify and eliminate system bottlenecks to deliver low-latency, high-throughput performance for real-time voice AI.

Requirements :

Core Experience :

- 5+ years of experience in backend/platform/infrastructure engineering.

- Proven track record building and scaling distributed systems in production environments.

- Deep knowledge of Kubernetes (EKS/GKE) - including custom operators, CRDs, and cluster operations.

- Proficiency in AWS (preferred) or GCP, using Infrastructure-as-Code tools (Terraform, CloudFormation).

- Strong experience with monitoring and observability stacks (Prometheus, Grafana, ELK).

- Proficient in Python (especially with FastAPI, asyncio) and/or Go.

Systems and Performance :

- Experience with high-throughput, low-latency systems (ideally in real-time domains).

- Deep understanding of systems design, caching (Redis), and messaging queues (Kinesis, SQS).

- Strong expertise with PostgreSQL and Redis.

- Knowledge of networking, service mesh, and security best practices in cloud environments.

Bonus Skills (Nice to Have) :

- Experience deploying and monitoring machine learning models in production.

- Familiarity with GPU orchestration (NVIDIA, CUDA) and real-time inference optimization.

- Knowledge of PyTorch, TensorFlow, or similar ML frameworks.

- Familiarity with WebRTC, real-time audio processing, or streaming architecture optimisations.

- Experience with multi-tenant SaaS platforms.