Lead DevOps / Cloud Engineer — Multimodal Search Platform

Key responsibilities

• - End-to-end infrastructure ownership: Design, deploy, and operate scalable cloud infrastructure supporting a high-scale multimodal search platform handling production traffic across text, image, and voice workloads.

• - Kubernetes platform engineering: Architect and manage Kubernetes-based production environments (EKS/GKE/AKS) with robust autoscaling, failover mechanisms, and zero-downtime deployment practices.

• - Infrastructure as Code (IaC): Design and maintain reproducible infrastructure using Terraform or equivalent tools to ensure secure, version-controlled environments across development, staging, and production.

• - Reliability & performance engineering: Implement high-availability and low-latency architectures capable of handling traffic spikes through intelligent caching, queuing, rate limiting, load balancing, and graceful degradation strategies.

• - CI/CD & release automation: Build and maintain automated CI/CD pipelines enabling safe, repeatable, and rapid deployments with rollback capabilities and environment parity.

• - Disaster recovery & incident management: Establish redundancy, backup, and disaster recovery systems aligned with defined SLOs/SLAs; lead incident response practices including runbooks and post-incident reviews.

• - Observability & monitoring: Implement comprehensive observability covering metrics, logs, distributed tracing, alerting, and performance monitoring; drive continuous reliability improvements through data-driven insights.

• - AI infrastructure & model serving: Support GPU-based inference infrastructure and AI model deployment pipelines (e.g., Triton, vLLM, TGI), collaborating closely with MLOps and ML teams to ensure reliable, scalable model serving under production workloads.

• - Security & operational excellence: Enforce cloud security best practices, access controls, secrets management, and infrastructure governance aligned with enterprise deployment standards.

• - Cost optimization: Continuously monitor and optimize cloud resource utilization, GPU workloads, and infrastructure spend without compromising performance or reliability.

Required skills & experience

• - 6–10 years of experience in DevOps, Site Reliability Engineering (SRE), or Cloud Platform Engineering roles within high-scale technology environments.

• - Strong hands-on expertise with at least one major cloud platform — AWS, GCP, or Azure — including networking, compute, storage, and managed Kubernetes services.

• - Deep experience operating production Kubernetes environments at scale, including autoscaling, cluster upgrades, workload orchestration, and resilience design.

• - Proven experience implementing Infrastructure as Code using Terraform (preferred) or equivalent tooling.

• - Strong understanding of distributed systems reliability, including load balancing, caching strategies, asynchronous queues, and failure recovery patterns.

• - Experience designing and managing CI/CD pipelines using modern tooling (GitHub Actions, GitLab CI, ArgoCD, Jenkins, or equivalent).

• - Hands-on experience building observability stacks using tools such as Prometheus, Grafana, ELK/OpenSearch, Datadog, or OpenTelemetry.

• - Experience supporting GPU workloads and AI inference systems, including containerized model deployment and performance optimization for production ML systems.

• - Familiarity with AI model serving frameworks such as Triton Inference Server, vLLM, TGI, or similar platforms is strongly preferred.

• - Strong scripting and automation skills (Python, Bash, or Go preferred).

• - Solid understanding of networking, security best practices, secrets management, and cloud cost optimization strategies.

• - Experience working in fast-moving startup or scale-up environments with high ownership expectations.

Qualifications

• - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.

• - Relevant cloud certifications (AWS/GCP/Azure, Kubernetes CKA/CKAD) are preferred but not mandatory.

• - Demonstrated experience supporting production systems with defined uptime, latency, and reliability targets.

Why join KnowDis.ai

You will help build foundational infrastructure powering advanced multimodal AI systems used in real-world production environments. This role offers deep technical ownership, exposure to cutting-edge AI workloads, and the opportunity to shape platform reliability from the ground up within a fast-growing AI company.

Selection Process:

- Interested Candidates are mandatorily required to apply through this listing on Jigya. Only applications received through this posting will be evaluated further.

- Shortlisted candidates may be required to appear in a Screening interview administered by Jigya

- Candidates selected after the Jigya screening rounds will be interviewed by KnowDis