{"id":4010341,"url":"https://alion.io/job/valo-founding-platform-engineer-sre-devops-ml-infra","title":"Founding Platform Engineer (SRE / DevOps / ML Infra)","company":{"id":4488327,"name":"Valo","domain":"tellvalo.com","url":"https://alion.io/company/valo-4","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"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":"LLM","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":"Triton","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-11-10T09:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-10T09:35:30Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"We are looking for an exceptional Founding Platform Engineer with deep expertise in Site Reliability Engineering (SRE), DevOps, cloud infrastructure, and production ML systems. This is a high-ownership, hands-on engineering role where you will architect, build, and scale the foundational infrastructure powering Valo's AI agents, voice systems, and machine learning workloads.\n\nThe ideal candidate has spent at least 5 years in SRE/DevOps or platform engineering and has personally owned, deployed, and maintained at least one business-critical ML system in production. You should bring strong expertise in cloud infrastructure, distributed systems, data engineering, and production reliability, with the ability to make critical architectural decisions independently.\n\nThe candidate will have responsibilities across the following functions: \n\nCloud Infrastructure and Platform Engineering: \nArchitect, build, and operate highly scalable, secure, and resilient cloud infrastructure, primarily on AWS.\nDesign and manage containerised workloads using Kubernetes, ECS, and Docker.\nImplement Infrastructure as Code using Terraform, AWS CDK, or equivalent tools.\nBuild robust CI/CD pipelines, automated deployment workflows, and safe rollback mechanisms.\nDesign infrastructure capable of supporting millions of users and large-scale concurrent workloads.\n\nProduction ML Infrastructure and 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 LLMs and speech models.\nManage GPU-based infrastructure, inference services, model serving, and workload orchestration.\nOptimise model-serving infrastructure for latency, throughput, reliability, and cost efficiency.\nEnsure production ML systems meet strict uptime, monitoring, and recovery requirements.\n\nData Engineering and 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 systems.\nCollaborate with AI/ML engineers to support data and model infrastructure requirements.\n\nSite Reliability Engineering and Observability: \nEstablish end-to-end observability across infrastructure, application services, and ML workloads.\nImplement monitoring, logging, tracing, alerting, and incident-response frameworks.\nDefine and manage SLIs, SLOs, and operational reliability standards.\nOwn production incident management, root-cause analysis, and post-incident improvements.\nProactively identify infrastructure bottlenecks and eliminate recurring reliability issues.\n\nInfrastructure Security and Scalability: \nDesign secure infrastructure for sensitive user data, credentials, payments, and AI-agent operations.\nImplement IAM, secrets management, network isolation, access controls, and infrastructure hardening.\nArchitect isolated execution environments for concurrent AI agents and browser-based workloads.\nEnsure secure, fault-tolerant execution of long-running workflows and critical transactions.\n\nRequirements: \n5+ years of hands-on experiencein SRE, DevOps, infrastructure engineering, or platform engineering.\nMandatory: Direct ownership of at least one mission-critical, production-grade ML system, including its deployment, monitoring, scaling, and operational reliability.\nExceptional understanding of AWS cloud infrastructure, networking, compute, storage, security, and distributed systems.\nStrong hands-on expertise withKubernetes/ECS, Docker, Terraform/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 and own infrastructure in a fast-moving startup environment.\n\nStrongly Preferred: \nExperience managing GPU infrastructure and production LLM/ML inference systems.\nExposure to model-serving technologies such as vLLM, NVIDIA Triton, or equivalent.\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 being an early infrastructure hire.\nSkills\nAWS, CI / CD, DevOps, Docker, Kubernetes, Platform, Python, SRE, Terraform","description_format":"text","description_chars":5089,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","AI 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