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    Job · 21051285

    CHENNAI · FULLTIME

    Senior AI Platform / MLOps Engineer - LLM Infrastructure - IMMEDIATE Joiner - Chennai

    datavruti

    datavruti

    Chennai · onsite · Posted 8d ago

    Sourced fromFoundit
    Not disclosed6–12 yrsfulltimeChennai

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    Section · 01

    About this role

    Hiring for: A US-based, AI-first data solutions company founded by seasoned technology leaders.

    Role: Senior AI Platform Engineer - MLOps & LLM Serving - Chennai (Hybrid)

    Positions: 1

    Experience: 6 to 12 years

    Location(s): Remote (Global), Chennai

    Type: Hybrid / Permanent

    Salary: Up to INR 25 LPA

    Notice Period: Immediate to 30 days

    Key Responsibilities:

    • Install, configure and operate OpenShift, NVIDIA GPU operator, OpenShift AI, and NIM microservices on 12× RTX PRO 6000 across two servers; single-node and HA control- plane topologies

    • Serving configuration and tuning: quantized model deployment (FP8/FP4), replica balancing, batching, KV-cache and context management

    • Azure GPU build environments: provisioning, cost control, parity with the on-prem stack via pinned container/model versions; cloud-to-factory migration with parity regression

    • GitOps CI/CD, container registry, artifact/model versioning, environment promotion;observability and audit wiring (Splunk, Prometheus/Grafana)

    • Benchmark automation: load harness, p50/p95/p99 latency, tokens/sec, GPU utilization; the capacity report data pipeline

    • Platform upgrade procedure with evaluation-regression gates; deployment runbook as a first-class deliverable

    Technical Skills:

    • 6+ years infrastructure/platform engineering with 3+ years production Kubernetes; OpenShift experience strongly preferred

    • Hands-on GPU inference serving in production: NIM, Triton, vLLM, or TensorRT-LLM you have sized, deployed, and tuned LLM serving on real GPUs and can talk memory- bandwidth trade-offs

    • GitOps fluency (ArgoCD/Flux), infrastructure-as-code, container internals; comfortable in air-gapped/proxy-restricted enterprise networks

    • Observability depth: metrics, traces, log pipelines; has built performance test harnesses, not just run them

    • Azure or AWS GPU compute operations experience

    Strongly preferred

    • NVIDIA GPU operator and AI Enterprise stack specifics; KServe; Milvus or pgvector operations; VAST/NFS/S3 storage integration; banking or other regulated-environment delivery

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    Section · 02

    Skills

    observabilityOpenshiftPrometheusGrafanaKubernetesAzure GPU build environmentsNIM microservicesamazon web servicesinfrastructure as codeGitOpsvLLMTritonContainer RegistryTensorRT-LLMNVIDIA GPU OperatorSplunkMicrosoft Azure

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