{"id":4061895,"url":"https://alion.io/job/weekday-founding-platform-engineer","title":"Founding Platform Engineer","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":80,"open_postings":105,"ghost_share":0,"stale_share":0.981,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-11-11T05:49:22Z"}},"role":"DevOps","role_family":"DevOps","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Gurgaon, India","Noida, India","Delhi, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS CDK","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Redis","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Terraform","optional":false},{"name":"LLM","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-11T09:04:19Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-11T09:04:19Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"This role is for one of Weekday’s clients\nMin Experience: 5+ years\nLocation: NCR, Gurgaon, Haryana, India\nJobType: full-time\nWe 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.\nIn 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.\nYou 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.\nA key requirement is direct ownership of at least one production-grade ML system, including its deployment, monitoring, scaling, and operational reliability.\nRequirements\nKey Responsibilities\nCloud Infrastructure & Platform Engineering\nArchitect, build, and operate scalable, secure, and highly available cloud infrastructure, primarily on AWS.\nDesign and manage containerised workloads using Kubernetes, Amazon ECS, and Docker.\nImplement Infrastructure as Code (IaC) using Terraform, AWS CDK, or equivalent tools.\nBuild and maintain robust CI/CD pipelines, automated deployment workflows, and safe rollback mechanisms.\nDesign infrastructure capable of supporting millions of users and large-scale concurrent workloads.\nOptimise cloud infrastructure for performance, scalability, availability, and cost efficiency.\nProduction ML Infrastructure & Reliability\nOwn the deployment, scalability, availability, and operational reliability of mission-critical machine learning systems.\nBuild and maintain infrastructure for production ML inference workloads, including Large Language Models (LLMs) and speech models.\nManage GPU-based infrastructure, model-serving services, inference workloads, and execution orchestration.\nOptimise model-serving infrastructure for latency, throughput, reliability, and cost.\nEstablish monitoring, recovery, and uptime standards for production ML systems.\nCollaborate with AI/ML engineers to support model deployment, infrastructure requirements, and production performance.\nData Engineering & Distributed Systems\nDesign and maintain reliable data infrastructure, including databases, message queues, streaming systems, and distributed workers.\nWork with technologies such as PostgreSQL, Redis, Kafka, and other distributed data-processing systems.\nBuild scalable data pipelines supporting ML inference, application services, and operational workloads.\nEnsure data consistency, fault tolerance, and efficient processing across distributed environments.\nDesign and maintain reliable asynchronous processing and high-concurrency systems.\nSite Reliability Engineering & Observability\nEstablish end-to-end observability across infrastructure, application services, and ML workloads.\nImplement monitoring, logging, distributed tracing, alerting, and incident-response frameworks.\nDefine and manage Service Level Indicators (SLIs), Service Level Objectives (SLOs), and operational reliability standards.\nOwn production incident management, root-cause analysis, and post-incident improvements.\nIdentify infrastructure bottlenecks and proactively eliminate recurring reliability issues.\nBuild automation to improve system availability, operational efficiency, and recovery capabilities.\nInfrastructure Security & Scalability\nDesign secure infrastructure for sensitive user data, credentials, payments, and AI-agent operations.\nImplement Identity and Access Management (IAM), secrets management, network isolation, access controls, and infrastructure hardening.\nArchitect isolated execution environments for concurrent AI agents and browser-based workloads.\nEnsure secure and fault-tolerant execution of long-running workflows and critical transactions.\nEstablish infrastructure security practices that support scalability, resilience, and data protection.\nRequired Qualifications\n5+ years of hands-on experience in Site Reliability Engineering, DevOps, Infrastructure Engineering, or Platform Engineering.\nDirect ownership of at least one mission-critical, production-grade ML system, including deployment, monitoring, scaling, and operational reliability.\nExceptional understanding of AWS cloud infrastructure, including networking, compute, storage, security, and distributed systems.\nStrong hands-on expertise with Kubernetes, ECS, Docker, Terraform or AWS CDK, and CI/CD pipelines.\nStrong understanding of data engineering fundamentals, including data pipelines, databases, messaging systems, and distributed processing.\nExperience operating highly available, low-latency production systems at meaningful scale.\nProven ability to troubleshoot complex infrastructure issues and independently lead production incident resolution.\nStrong automation and scripting capabilities using Python, Bash, or similar languages.\nAbility to independently architect, build, and own infrastructure in a fast-paced startup environment.\nPreferred Qualifications\nExperience managing GPU infrastructure and production LLM/ML inference systems.\nExposure to model-serving technologies such as vLLM, NVIDIA Triton, or equivalent platforms.\nExperience with real-time voice AI, speech processing, or latency-sensitive inference workloads.\nExperience scaling high-concurrency systems, long-running workers, or isolated execution environments.\nBackground in high-scale AI/ML product companies, fintech platforms, or infrastructure-intensive SaaS businesses.\nExperience building platform infrastructure from scratch or working as an early infrastructure engineering hire.\nMust-Have Skills\nSite Reliability Engineering (SRE)\nDevOps\nAWS Cloud Infrastructure\nGood-to-Have Skills\nGPU Infrastructure\nLLM Inference\nSpeech 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