# Founding Platform Engineer > Weekday AI · Gurugram, India · Full-time · Posted 2026-11-11 **Workplace:** on_site **Department:** Weekday's Client via platform ## Description **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 secure and fault-tolerant execution of long-running workflows and critical transactions. - Establish infrastructure security practices that support scalability, resilience, and data protection. ## Required Qualifications - 5+ years of hands-on experience in Site Reliability Engineering, DevOps, Infrastructure Engineering, or Platform Engineering. - Direct ownership of at least one mission-critical, production-grade ML system, including deployment, monitoring, scaling, and operational reliability. - Exceptional understanding of AWS cloud infrastructure, including networking, compute, storage, security, and distributed systems. - Strong hands-on expertise with Kubernetes, ECS, Docker, Terraform or AWS CDK, and CI/CD pipelines. - Strong understanding of data engineering fundamentals, including data pipelines, databases, messaging systems, and distributed processing. - Experience operating highly available, low-latency production systems at meaningful scale. - Proven ability to troubleshoot complex infrastructure issues and independently lead production incident resolution. - Strong automation and scripting capabilities using Python, Bash, or similar languages. - Ability to independently architect, build, and own infrastructure in a fast-paced startup environment. ## Preferred Qualifications - Experience managing GPU infrastructure and production LLM/ML inference systems. - Exposure to model-serving technologies such as vLLM, NVIDIA Triton, or equivalent platforms. - Experience with real-time voice AI, speech processing, or latency-sensitive inference workloads. - Experience scaling high-concurrency systems, long-running workers, or isolated execution environments. - Background in high-scale AI/ML product companies, fintech platforms, or infrastructure-intensive SaaS businesses. - Experience building platform infrastructure from scratch or working as an early infrastructure engineering hire. ## Must-Have Skills - Site Reliability Engineering (SRE) - DevOps - AWS Cloud Infrastructure ## Good-to-Have Skills - GPU Infrastructure - LLM Inference - Speech Models ## Apply [Apply at Weekday AI](https://apply.workable.com/weekday-1/j/192A10F810/apply) --- Powered by [Workable](https://www.workable.com)