Lead DevOps Engineer (AI & GPU)

<h3>Accountabilities:</h3><li>Lead the design, implementation, and operation of CI/CD and continuous verification (CV) pipelines for AI services, agents, and runtime components, covering automated builds, testing, releases, validation, and rollback.</li><li>Implement and manage GitOps-based deployment workflows using tools such as Argo CD, Helm, GitHub Actions, GitLab CI, or equivalent technologies to ensure consistent and repeatable delivery.</li><li>Operate and optimize production-scale AI inference and model-serving infrastructure using frameworks such as Ray Serve, vLLM, NVIDIA Triton, or NVIDIA NIM across GPU-enabled environments.</li><li>Manage AI asset governance and discovery, including artifact and container registries, model metadata, experiment tracking, upstream model repositories, software supply-chain security, license management, and lineage tracking.</li><li>Integrate tools such as Harbor, Artifactory, Nexus, MLflow, Hugging Face, NVIDIA GPU Cloud (NGC), Trivy, and software bill of materials (SBOM) scanning into secure AI delivery workflows.</li><li>Design and maintain a comprehensive observability platform encompassing metrics, logs, traces, dashboards, alerts, service-level agreements (SLAs), and AI-specific debugging and evaluation capabilities.</li><li>Build and operate OpenTelemetry collection infrastructure, including multi-tenant receivers, exporters, processing pipelines, batching, dynamic filtering, and Kafka-based telemetry delivery.</li><li>Implement GPU monitoring and performance visibility through tools such as the NVIDIA Data Center GPU Manager (DCGM) exporter, enabling accurate utilization tracking and infrastructure optimization.</li><li>Establish AI FinOps practices by implementing quotas, rate limits, utilization monitoring, and per-tenant cost attribution using tools such as OpenCost, Kubecost, or equivalent platforms.</li><li>Ensure that AI workload consumption, telemetry, and runtime behavior are consistently associated with tenant and workload identities to support accountability, cost control, and end-to-end traceability.</li><li>Integrate security, quality, integrity, compliance, and cost controls into deployment pipelines, ensuring releases meet defined policies before reaching production.</li><li>Connect observability and evaluation signals to deployment decisions, enabling progressive rollouts, automated rollback, and continuous improvements based on operational performance.</li><li>Support secure and consistent AI runtime operations across public cloud, private infrastructure, edge deployments, and air-gapped environments.</li><li>Collaborate with platform engineering, security, trust, and data teams to establish clear ownership boundaries, service expectations, consumption policies, and operational contracts.</li><li>Communicate architectural decisions and technical trade-offs related to AI infrastructure, cost management, runtime reliability, and observability to engineers, architects, and leadership.</li><h2>Requirements:</h2><li>Bachelor's or Master's degree in Computer Science or a related technical discipline.</li><li>At least 8 years of professional experience in DevOps, MLOps, platform engineering, observability engineering, or related fields, including hands-on production ownership of AI workloads or high-throughput services.</li><li>Strong expertise in CI/CD pipeline design, continuous verification, GitOps practices, automated testing, progressive delivery, and production release and rollback strategies.</li><li>Practical experience with GitOps and automation tools such as Argo CD, Helm, GitHub Actions, GitLab CI, or equivalent technologies.</li><li>Demonstrated experience operating production-scale AI inference and model-serving systems, including Ray Serve, vLLM, NVIDIA Triton, or NVIDIA NIM, on GPU infrastructure.</li><li>Strong understanding of GPU-enabled workloads, inference performance optimization, resource allocation, and the operational requirements of high-throughput AI services.</li><li>Extensive knowledge of observability technologies, including Prometheus, Grafana, Loki, Tempo, Jaeger, and OpenTelemetry, covering metrics, logs, distributed tracing, alerting, and dashboard design.</li><li>Experience with API gateways and request-routing technologies such as Envoy or equivalent, including support for streaming responses and efficient AI request distribution.</li><li>Familiarity with AI model and artifact governance, container registries, software supply-chain security, vulnerability scanning, SBOMs, license compliance, and model lineage.</li><li>Experience implementing cost-management and FinOps practices using OpenCost, Kubecost, or equivalent tools, including resource quotas, rate limiting, utilization monitoring, and workload-level cost attribution.</li><li>Ability to design and operate infrastructure consistently across cloud, on-premises, edge, and air-gapped environments, with an emphasis on security, reliability, and operational consistency.</li><li>Proven experience managing production infrastructure subject to security controls, compliance requirements, or regulatory constraints.</li><li>Strong analytical and problem-solving skills, with the ability to troubleshoot complex distributed systems and improve performance, reliability, and cost efficiency.</li><li>Excellent communication and collaboration skills, including the ability to explain technical architecture, operational risks, and cost trade-offs to both technical and non-technical stakeholders.</li><li>Ability to establish clear service ownership, consumption contracts, and operational expectations across platform, security, trust, and data teams.</li><li>Experience with LLM evaluation and debugging tools such as LangSmith or Langfuse, including prompt and response quality measurement, is an advantage.</li><li>Familiarity with secure sandboxing and workload isolation technologies such as gVisor, Firecracker, or microVMs is beneficial.</li><li>Additional experience with GPU telemetry through DCGM, OpenLineage, AI license management, multi-tenant SLA monitoring, and per-tenant alerting is desirable.</li><li>Exposure to regulated industries such as financial services, healthcare, taxation, or risk management is an advantage.</li><h2>Benefits:</h2><li>
<strong>Remote work flexibility:</strong>Opportunity to work remotely from India, with Bangalore identified as the role's location.</li><li>
<strong>Full-time employment:</strong>Join a technical team focused on building and operating advanced AI infrastructure and production-grade systems.</li><li>
<strong>Technical leadership opportunities:</strong>Influence AI platform architecture, deployment standards, observability strategies, and GPU infrastructure operations.</li><li>
<strong>Advanced technology exposure:</strong>Work with modern AI inference frameworks, GPU computing, cloud-native infrastructure, GitOps, and distributed observability platforms.</li><li>
<strong>Professional development:</strong>Expand your expertise in AI infrastructure, MLOps, FinOps, platform engineering, and secure production deployment.</li><li>
<strong>Cross-functional collaboration:</strong>Partner with engineering, platform, security, and data teams to solve complex infrastructure challenges.</li><li>
<strong>Meaningful technical impact:</strong>Improve the reliability, scalability, security, transparency, and cost efficiency of production AI workloads.</li><li>
<strong>Inclusive workplace:</strong>Opportunity to contribute to a professional environment committed to equal employment opportunities.</li><li>
<strong>Salary and additional benefits:</strong>Compensation details, healthcare coverage, paid leave, bonuses, and other specific benefits have not been disclosed in the available job description and should be confirmed during the hiring process.</li>