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