AI ML Ops Engineer

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Designation: Senior Software Engineer - Analytics

Location : Indore

Educational Qualifications : B. E/B. Tech

Exp : 3-5 Years

Mode of work : Hybrid

Role Overview

We are seeking an AI/ML Ops Engineer to own the deployment, monitoring, and scaling of ML models and pipelines in production. You will build the infrastructure and automation that lets models move reliably from training to production and stay healthy once there.

Key Responsibilities

• CI/CD for ML: Build and maintain automated pipelines for model training, testing, and deployment.
• Infrastructure: Containerize and orchestrate model-serving infrastructure (Docker, Kubernetes) at scale.
• Monitoring: Set up monitoring for model performance, data/concept drift, latency, and system health.
• Versioning Reproducibility: Manage model/experiment versioning and reproducibility (MLflow, DVC, or similar).
• Cloud GPU Management: Manage scalable, cost-efficient GPU/cloud infrastructure for training and inference workloads.

Technical Requirements

• Strong hands-on experience with Docker and Kubernetes
• CI/CD tooling (GitHub Actions, Jenkins, GitLab CI, or similar)
• Experience with model-serving frameworks (Triton Inference Server, TorchServe, or similar)
• Cloud platform experience (AWS/Azure/GCP), especially GPU infrastructure
• Python for automation and tooling
• Experience with experiment tracking and model registries (MLflow, DVC, Weights Biases)
• Monitoring/observability tooling (Prometheus, Grafana, or similar)
• Production experience with Triton Inference Server: model repositories, ensembles, dynamic batching, instance groups
• GPU operations on Kubernetes: NVIDIA GPU Operator, MIG/time-slicing, node pools, driver/CUDA version management
• Hands-on with TensorRT / ONNX Runtime conversion and performance profiling (Nsight, perf_analyzer) gRPC and streaming service patterns; load testing tools (Locust, k6)
• Strong Linux, networking and debugging fundamentals for bare-metal environments

Mandatory Skills

• Docker, Kubernetes, CI/CD, Python, cloud infrastructure (AWS/Azure/GCP)

Good to Have

• Experience deploying LLM/NLP inference services specifically (batching, quantization, low-latency serving)
• Familiarity with vLLM, Ollama, or similar for local LLM hosting
• Infrastructure-as-code experience (Terraform, Helm)
• Experience with NVIDIA NVCF, NeMo or NIM deployments
• Serving TTS/ASR models where time-to-first-byte matters
• Log/trace stacks (Loki, OpenTelemetry, ELK) and on-call tooling

Required Skills

Linus GCP CICD kubernetes azure monitoring tools python Docker AWS

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.