Founding Platform Engineer

This role is for one of Weekday’s clients

Min Experience: 5+ years Location: NCR, Gurgaon, Haryana, India JobType: full-time

We are looking for an exceptional Founding Platform Engineer with deep expertise in Site Reliability Engineering (SRE), DevOps, cloud infrastructure, and production Machine Learning (ML) systems. In this high-ownership role, you will architect, build, and operate the core platform infrastructure supporting large-scale AI-powered applications. You will own critical infrastructure components across cloud architecture, production ML deployment, distributed systems, observability, security, and reliability. You will work closely with the founding team and AI/ML engineers to build scalable, secure, and resilient infrastructure capable of supporting millions of users, high-concurrency workloads, and mission-critical AI operations. A key requirement is direct ownership of at least one production-grade ML system, including its deployment, monitoring, scaling, and operational reliability. Requirements Key Responsibilities Cloud Infrastructure & Platform Engineering - Architect, build, and operate scalable, secure, and highly available cloud infrastructure, primarily on AWS. - Design and manage containerised workloads using Kubernetes, Amazon ECS, and Docker. - Implement Infrastructure as Code (IaC) using Terraform, AWS CDK, or equivalent tools. - Build and maintain robust CI/CD pipelines, automated deployment workflows, and safe rollback mechanisms. - Design infrastructure capable of supporting millions of users and large-scale concurrent workloads. - Optimise cloud infrastructure for performance, scalability, availability, and cost efficiency. Production ML Infrastructure & Reliability - Own the deployment, scalability, availability, and operational reliability of mission-critical machine learning systems. - Build and maintain infrastructure for production ML inference workloads, including Large Language Models (LLMs) and speech models. - Manage GPU-based infrastructure, model-serving services, inference workloads, and execution orchestration. - Optimise model-serving infrastructure for latency, throughput, reliability, and cost. - Establish monitoring, recovery, and uptime standards for production ML systems. - Collaborate with AI/ML engineers to support model deployment, infrastructure requirements, and production performance. Data Engineering & Distributed Systems - Design and maintain reliable data infrastructure, including databases, message queues, streaming systems, and distributed workers. - Work with technologies such as PostgreSQL, Redis, Kafka, and other distributed data-processing systems. - Build scalable data pipelines supporting ML inference, application services, and operational workloads. - Ensure data consistency, fault tolerance, and efficient processing across distributed environments. - Design and maintain reliable asynchronous processing and high-concurrency systems. Site Reliability Engineering & Observability - Establish end-to-end observability across infrastructure, application services, and ML workloads. - Implement monitoring, logging, distributed tracing, alerting, and incident-response frameworks. - Define and manage Service Level Indicators (SLIs), Service Level Objectives (SLOs), and operational reliability standards. - Own production incident management, root-cause analysis, and post-incident improvements. - Identify infrastructure bottlenecks and proactively eliminate recurring reliability issues. - Build automation to improve system availability, operational efficiency, and recovery capabilities. Infrastructure Security & Scalability - Design secure infrastructure for sensitive user data, credentials, payments, and AI-agent operations. - Implement Identity and Access Management (IAM), secrets management, network isolation, access controls, and infrastructure hardening. - Architect isolated execution environments for concurrent AI agents and browser-based workloads. - Ensure s