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MLOps / Serving Engineer

Hyderabad, Telangana, India

Pay
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Work setting
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Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Deploy fine-tuned LLMs with vLLM, TensorRT-LLM, or Triton on AWS GPU instances

  • Run shadow-mode deployments that log model comparisons without affecting live traffic

  • Execute staged canary and ramp rollouts with automated rollback on quality drops

  • Optimize inference for long inputs: prefill throughput, KV-cache, INT8 quantization

  • Build monitoring dashboards for latency, throughput, accuracy and cost per request

From the employer’s posting
Deploy fine-tuned LLMs using vLLM, TensorRT-LLM, or Triton with continuous batching on AWS GPU instances
Build shadow-mode deployment: run fine-tuned model alongside production, log comparison data without impacting live traffic
Execute staged rollout: canary (5%) → gradual ramp (25% → 50% → 100%) with automated rollback on quality degradation
Optimize inference for input-heavy workloads (~17K token inputs, ~130 token outputs): prefill throughput, KV-cache, INT8 quantization
Build monitoring dashboards: latency, throughput, accuracy metrics, cost per request

Tools in this posting

  • AWS
  • Docker
  • Grafana
  • Kubernetes
Source — Tool mentions in context
Job Title: MLOps / Serving Engineer Experience : 5+ years Location : Hyderabad OR Pune Notice Period: 0-30 days Work mode - Hybrid We are seeking an experienced MLOps / Serving Engineer who can design and operate the production serving infrastructure for fine-tuned LLMs on AWS — optimised inference engines, shadow-mode and staged rollout pipelines, monitoring dashboards, and the path from experimental model to full production traffic. Key Responsibilities: Deploy fine-tuned LLMs using vLLM, TensorRT-LLM, or Triton with continuous batching on AWS GPU instances Build shadow-mode deployment: run fine-tuned model alongside production, log comparison data without impacting live traffic Execute staged rollout: canary (5%) → gradual ramp (25% → 50% → 100%) with automated rollback on quality degradation Optimize inference for input-heavy workloads (~17K token inputs, ~130 token outputs): prefill throughput, KV-cache, INT8 quantization Build monitoring dashboards: latency, throughput, accuracy metrics, cost per request Design auto-scaling; implement high-availability (2× instances); automated rollback triggers on end-to-end quality metrics Requirements 5+ years MLOps or ML infrastructure engineering Hands-on with vLLM, TensorRT-LLM, or Triton Inference Server Deep familiarity with g5, p4de, p5 instance families, EC2 auto-scaling Have worked on Deployment patterns like Shadow-mode, canary, A/B traffic routing, automated rollback Experience onto Continuous batching, INT8 quantization, KV-cache management Expertise on Docker, Kubernetes (EKS) for ML workloads Worked on CloudWatch, Prometheus, Grafana Benefits Comprehensive Medical Coverage: Health insurance of INR 5.0 Lakhs for you and your family (up to 6 members), ensuring complete peace of mind. Robust Protection Plans: Group Personal Accident Insurance and Group Term Life Insurance to safeguard you and your loved ones. Retirement Benefits: PF and Gratuity provided as per standard government regulations. Flexible Work Options: Enjoy hybrid work arrangements & flexible working hours Generous Leave Policy: 21 days of annual leave, in addition to 10 company-declared holidays. Employee Well-being Spaces: Access to a dedicated break-out area with round-the-clock refreshments for relaxation and rejuvenation.

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Live
First seen by us
Sep 8, 2026 · 51 days ago
Recorded sightings
34
Last seen by us
Oct 29, 2026

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Job description

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Job Title: MLOps / Serving Engineer Experience : 5+ years Location : Hyderabad OR Pune Notice Period: 0-30 days Work mode - Hybrid We are seeking an experienced MLOps / Serving Engineer who can design and operate the production serving infrastructure for fine-tuned LLMs on AWS — optimised inference engines, shadow-mode and staged rollout pipelines, monitoring dashboards, and the path from experimental model to full production traffic. Key Responsibilities: Deploy fine-tuned LLMs using vLLM, TensorRT-LLM, or Triton with continuous batching on AWS GPU instances Build shadow-mode deployment: run fine-tuned model alongside production, log comparison data without impacting live traffic Execute staged rollout: canary (5%) → gradual ramp (25% → 50% → 100%) with automated rollback on quality degradation Optimize inference for input-heavy workloads (~17K token inputs, ~130 token outputs): prefill throughput, KV-cache, INT8 quantization Build monitoring dashboards: latency, throughput, accuracy metrics, cost per request Design auto-scaling; implement high-availability (2× instances); automated rollback triggers on end-to-end quality metrics Requirements 5+ years MLOps or ML infrastructure engineering Hands-on with vLLM, TensorRT-LLM, or Triton Inference Server Deep familiarity with g5, p4de, p5 instance families, EC2 auto-scaling Have worked on Deployment patterns like Shadow-mode, canary, A/B traffic routing, automated rollback Experience onto Continuous batching, INT8 quantization, KV-cache management Expertise on Docker, Kubernetes (EKS) for ML workloads Worked on CloudWatch, Prometheus, Grafana Benefits Comprehensive Medical Coverage: Health insurance of INR 5.0 Lakhs for you and your family (up to 6 members), ensuring complete peace of mind. Robust Protection Plans: Group Personal Accident Insurance and Group Term Life Insurance to safeguard you and your loved ones. Retirement Benefits: PF and Gratuity provided as per standard government regulations. Flexible Work Options: Enjoy hybrid work arrangements & flexible working hours Generous Leave Policy: 21 days of annual leave, in addition to 10 company-declared holidays. Employee Well-being Spaces: Access to a dedicated break-out area with round-the-clock refreshments for relaxation and rejuvenation.
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