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
- Solid 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

📌 Founding Platform Engineer (Noida)
🏢 Weekday AI (YC W21)
📍 Noida