Platform Engineer

NextDay AI; Pakistan; Remote; PKR 24,910,000 - 33,213,000; Full time

Benefits offered by this job

Real 100% remote

Flex-time

Unlimited vacations

Job summary

NextDay AI is seeking a Platform Engineer to own and scale the infrastructure behind our consumer AI products. You will run a GPU inference fleet, operate EKS clusters, and advance automation, CI/CD, and observability.

The role emphasizes end-to-end ownership and hands-on work on production systems at scale. You will collaborate with a small, driven team to improve deployment pipelines, FinOps, and incident readiness while supporting AI-enabled services for millions of users.

Qualifications

• 3+ years in Platform/DevOps/SRE or infrastructure engineering in production.
• Strong Linux, networking, and cloud fundamentals.
• Experience with Kubernetes, AWS EKS, and GitOps workflows.
• Proficient in IaC (Terraform) and configuration management (Ansible).
• Fluent in building automation, scripts, and small services (Go/Python/TypeScript).
• Hands-on with AI coding tools and incident response practices.
• Solid observability: Prometheus, Grafana, and logging pipelines (Fluent Bit/Axiom).
• Experience with CI/CD (GitHub Actions) and GitOps pipelines.
• Experience with GPU workloads and multi-provider fleets.
• Strong written communication; documenting runbooks and decisions.

Responsibilities

• Operate and grow our GPU inference fleet across providers, provisioning nodes and tuning inference engines.
• Build automation for capacity management, including pre-warming and metric-driven autoscaling.
• Manage EKS clusters (ArgoCD, Autoscaler, CoreDNS, LB controller), including upgrades.
• Improve CI/CD on GitHub for faster, safer production deployments.
• Extend observability with Grafana dashboards, Prometheus metrics, and structured logging.
• Contribute to FinOps: track spend, identify savings, and translate into tickets.
• Document runbooks, architecture decisions, and processes in Confluence.

Job description

NextDay AI is a privately held company and world leader specializing in applying artificial intelligence (AI) engineering and creative innovation in B2C entertainment products. Our mission is to leverage the power of Large Language Models (LLM) and generative AI (Gen AI) to create innovative consumer products that push the boundaries of entertainment and companionship. This is a unique opportunity to participate and make a real difference in a company actively working to shape the future of AI applications for consumers.

About the Role

We're looking for a Platform Engineer to join our Platform team, the group that owns the infrastructure behind our consumer AI products.

The team runs a large, multi-provider GPU inference fleet, the routing layer that fronts it, our Kubernetes (EKS) clusters, observability and CI/CD, and the incident management practice around all of it. Everything the company ships to millions of users runs through this platform.

This role is ideal for a mid-level or senior engineer who enjoys operating real production systems at scale and wants to raise the level of automation around them. You'll spend most of your time hands-on: provisioning and tuning GPU inference nodes, evolving our HAProxy routing layer, moving infrastructure into Terraform and GitOps, improving deployment pipelines and building the dashboards and alerts that keep us ahead of incidents.

Ownership matters here: we're a small team with a wide surface, so we value people who take a problem end to end rather than waiting for a ticket to be fully specified.

What the ideal candidate will have:

• 3+ years of experience as a Platform, DevOps, SRE or Infrastructure Engineer running production systems in a startup or scale-up environment.
• Kubernetes experience, ideally on AWS EKS, including GitOps-based deployments (ArgoCD or similar) and cluster lifecycle operations (upgrades, autoscaling, node groups).
• Solid AWS background: EC2, VPC and networking, load balancers (ALB/NLB), IAM, CloudWatch, S3, and comfort reading and reasoning about a cloud bill.
• Infrastructure as Code with Terraform, plus configuration management with Ansible.
• Linux and networking fundamentals: systemd, nftables/iptables, DNS, TLS certificates, SSH, and debugging with logs and metrics rather than guesswork.
• Observability experience with Prometheus and Grafana (dashboards, alert rules, PromQL) and a structured logging pipeline (we use Fluent Bit and Axiom).
• CI/CD with GitHub Actions, including building and maintaining pipelines.
• Scripting and tooling skills in Go, Python or TypeScript; you write small services and CLIs to automate operations, not just shell scripts.
• Hands‑on, daily use of AI coding tools (Claude Code, Cursor, Copilot or similar) as part of how you work: writing and reviewing code, debugging incidents, and drafting docs. You know where they speed you up, where they get things wrong, and how to check their output before it reaches production.
• Interest in helping other engineers adopt AI tools well: building shared skills, prompts, MCP integrations or agent workflows that live alongside the code, and setting sensible guardrails for how they’re used.
• Experience running or supporting GPU workloads.
• Familiarity with serverless and event‑driven AWS backends — Lambda, Serverless Framework, DynamoDB, SQS, EventBridge.
• Clear written communication: you document runbooks, decisions and analyses in Confluence and Jira so the rest of the team can act without you.

Key Responsibilities

• Operate and grow our GPU inference fleet across multiple providers (bare‑metal, rented and cloud GPU nodes): provision new nodes with Ansible, deploy and tune inference engines, and register them in the routing layer.
• Build automation for capacity management: scheduled pre‑warming and metric‑driven scale up/down of elastic GPU nodes and hosted endpoints, replacing manual scaling during peak hours.
• Operate our EKS clusters (ArgoCD, Cluster Autoscaler, CoreDNS, load balancer controller), including version upgrades and right‑sizing.
• Improve CI/CD on GitHub: faster PR checks, more frequent and safer production deploys.
• Extend observability: Grafana dashboards and alert rules, Prometheus metrics for GPU nodes and proxies, structured logging with Fluent Bit and Axiom, and per‑request cost attribution.
• Contribute to FinOps: track inference and cloud spend, identify savings (right‑sizing, provider changes, network egress) and turn them into tickets and results.
• Document runbooks, architecture and decisions in Confluence so knowledge lives with the team rather than with one person.

Why Join Us?

• Real 100% remote work (we don't have physical offices to go to "once per week")
• Flex‑time / Manage your schedule Policy (we are looking for teamwork and results)
• Unlimited Vacations Policy
• A dynamic and supportive work environment.
• Opportunity to work closely with owners and leaders in a fast-growing company at the forefront of the AI revolution.
• Real scale: you'll run a GPU fleet serving LLM traffic to millions of users, with direct impact on product reliability and company cost.