Pl fill this short google form or ping me at 9910803550 Form: https://forms.gle/EbZ88d2tyX7HjSAh9
Valo is building an AI-powered personal assistant for every Indian — one that doesn't just answer questions but gets real-world tasks done. From making calls and managing bookings to coordinating services and payments,
Valo is building autonomous AI agents that plan, execute, and deliver verified outcomes.
We are a seed-funded startup founded by experienced leaders from Zomato, IDFC FIRST Bank, Kapture CX, Microsoft, and Grab, building proprietary AI models and infrastructure for millions of users.
We are looking for an exceptional Founding Platform Engineer with deep expertise in Site Reliability Engineering (SRE), DevOps, cloud infrastructure, and production ML systems. Key Responsibilities Cloud Infrastructure & Platform Engineering Architect, build, and operate highly scalable, secure, and resilient cloud infrastructure, primarily on AWS. Design and manage containerized workloads using Kubernetes, ECS, and Docker. Implement Infrastructure as Code using Terraform, AWS CDK, or equivalent tools.
Build robust CI/CD pipelines, automated deployment workflows, and safe rollback mechanisms. Design infrastructure capable of supporting millions of users and large-scale concurrent workloads. 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 LLMs and speech models. Manage GPU-based infrastructure, inference services, model serving, and workload orchestration. Optimize model-serving infrastructure for latency, throughput, reliability, and cost efficiency. Ensure production ML systems meet strict uptime, monitoring, and recovery requirements. 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 systems. Collaborate with AI/ML engineers to support data and model infrastructure requirements. Site Reliability Engineering & Observability Establish end-to-end observability across infrastructure, application services, and ML workloads. Implement monitoring, logging, tracing, alerting, and incident-response frameworks. Define and manage SLIs, SLOs, and operational reliability standards.
Own production incident management, root-cause analysis, and post-incident improvements. Proactively identify infrastructure bottlenecks and eliminate recurring reliability issues. Infrastructure Security & Scalability Design secure infrastructure for sensitive user data, credentials, payments, and AI-agent operations. Implement IAM, secrets management, network isolation, access controls, and infrastructure hardening. Architect isolated execution environments for concurrent AI agents and browser-based workloads. Ensure secure, fault-tolerant execution of long-running workflows and critical transactions. Requirements: 5+ years of hands-on experience in SRE, DevOps, infrastructure engineering, or platform engineering.
Mandatory: Direct ownership of at least one mission-critical, production-grade ML system, including its deployment, monitoring, scaling, and operational reliability. Exceptional understanding of AWS cloud infrastructure, networking, compute, storage, security, and distributed systems. Strong hands-on expertise with Kubernetes/ECS, Docker, Terraform/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 and own infrastructure in a fast-moving startup environment. Strongly Preferred Experience managing GPU infrastructure and production LLM/ML inference systems. Exposure to model-serving technologies such as vLLM, NVIDIA Triton, or equivalent. 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 being an early infrastructure hire.
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