Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

What you’ll work on

We are looking for an MLOps Developer to own the model lifecycle, deployment pipelines, and operational health of this AI system.

Optimize and deploy PyTorch models (detection + video classification) to TensorRT and ONNX for real-time GPU inference across both server and edge hardware.
Build and maintain reproducible model training and evaluation pipelines with experiment tracking and dataset versioning
Design CI/CD workflows for model validation, packaging, and rollout to remotely deployed devices.

From the employer’s posting

We are looking for an MLOps Developer to own the model lifecycle, deployment pipelines, and operational health of this AI system. You will bridge the gap between model development and production, ensuring models are reliably trained, versioned, deployed, and monitored across diverse hardware environments.

Key Responsibilities Optimize and deploy PyTorch models (detection + video classification) to TensorRT and ONNX for real-time GPU inference across both server and edge hardware. Build and maintain reproducible model training and evaluation pipelines with experiment tracking and dataset versioning

Optimize and deploy PyTorch models (detection + video classification) to TensorRT and ONNX for real-time GPU inference across both server and edge hardware. Build and maintain reproducible model training and evaluation pipelines with experiment tracking and dataset versioning Design CI/CD workflows for model validation, packaging, and rollout to remotely deployed devices.

Build and maintain reproducible model training and evaluation pipelines with experiment tracking and dataset versioning Design CI/CD workflows for model validation, packaging, and rollout to remotely deployed devices. Monitor production inference pipelines — GPU utilization, latency, frame throughput, and model performance drift

What you’ll bring

All qualifications

Core experience

2–4 years of MLOps or ML engineering experience in production.
Strong Python; hands-on with PyTorch, ONNX, and TensorRT
Experience with CI/CD tools, Docker, and Linux/bash environments
Familiarity with experiment tracking (MLflow, ClearML, W&B, or similar)

Qualification wording

2–4 years of MLOps or ML engineering experience in production.

Strong Python; hands-on with PyTorch, ONNX, and TensorRT

Experience with CI/CD tools, Docker, and Linux/bash environments

Familiarity with experiment tracking (MLflow, ClearML, W&B, or similar)

Tools in this posting

Python
AWS
Azure
Docker
S3
SageMaker
Bash
Kubernetes
MLflow
PyTorch

Source

- 2–4 years of MLOps or ML engineering experience in production. - Strong Python; hands-on with PyTorch, ONNX, and TensorRT - Experience with CI/CD tools, Docker, and Linux/bash environments

- Monitor production inference pipelines — GPU utilization, latency, frame throughput, and model performance drift - Manage cloud storage (AWS S3) for model artifacts, video clips, and deployment assets - Containerize services (Docker) and investigate Kubernetes-based orchestration for multi-site deployments for edge hardware environments

- Familiarity with experiment tracking (MLflow, ClearML, W&B, or similar) - AWS or Azure cloud experience with exposure to ML-specific services such as: - AWS: SageMaker (training jobs, model registry, endpoints), ECR, S3, Lambda, CloudWatch

- AWS or Azure cloud experience with exposure to ML-specific services such as: - AWS: SageMaker (training jobs, model registry, endpoints), ECR, S3, Lambda, CloudWatch - Azure: Azure Machine Learning (pipelines, model registry, compute clusters), Azure Container Registry, Blob Storage, Azure Monitor

- AWS: SageMaker (training jobs, model registry, endpoints), ECR, S3, Lambda, CloudWatch - Azure: Azure Machine Learning (pipelines, model registry, compute clusters), Azure Container Registry, Blob Storage, Azure Monitor Nice to Have

- Manage cloud storage (AWS S3) for model artifacts, video clips, and deployment assets - Containerize services (Docker) and investigate Kubernetes-based orchestration for multi-site deployments for edge hardware environments - Collaborate with ML and CV engineers to operationalize new model versions and document deployment runbooks

- Strong Python; hands-on with PyTorch, ONNX, and TensorRT - Experience with CI/CD tools, Docker, and Linux/bash environments - Familiarity with experiment tracking (MLflow, ClearML, W&B, or similar)

- WebRTC or WebSocket-based streaming experience - Exposure to Docker / Kubernetes for model serving at scale

- Experience with CI/CD tools, Docker, and Linux/bash environments - Familiarity with experiment tracking (MLflow, ClearML, W&B, or similar) - AWS or Azure cloud experience with exposure to ML-specific services such as:

Key Responsibilities - Optimize and deploy PyTorch models (detection + video classification) to TensorRT and ONNX for real-time GPU inference across both server and edge hardware. - Build and maintain reproducible model training and evaluation pipelines with experiment tracking and dataset versioning

Job description

View original posting ↗

About the Role

We are looking for an MLOps Developer to own the model lifecycle, deployment pipelines, and operational health of this AI system. You will bridge the gap between model development and production, ensuring models are reliably trained, versioned, deployed, and monitored across diverse hardware environments.

Key Responsibilities

Optimize and deploy PyTorch models (detection + video classification) to TensorRT and ONNX for real-time GPU inference across both server and edge hardware.

Build and maintain reproducible model training and evaluation pipelines with experiment tracking and dataset versioning

Design CI/CD workflows for model validation, packaging, and rollout to remotely deployed devices.

Monitor production inference pipelines — GPU utilization, latency, frame throughput, and model performance drift

Manage cloud storage (AWS S3) for model artifacts, video clips, and deployment assets

Containerize services (Docker) and investigate Kubernetes-based orchestration for multi-site deployments for edge hardware environments

Collaborate with ML and CV engineers to operationalize new model versions and document deployment runbooks

Requirements

2–4 years of MLOps or ML engineering experience in production.

Strong Python; hands-on with PyTorch, ONNX, and TensorRT

Experience with CI/CD tools, Docker, and Linux/bash environments

Familiarity with experiment tracking (MLflow, ClearML, W&B, or similar)

AWS or Azure cloud experience with exposure to ML-specific services such as:

AWS: SageMaker (training jobs, model registry, endpoints), ECR, S3, Lambda, CloudWatch

Azure: Azure Machine Learning (pipelines, model registry, compute clusters), Azure Container Registry, Blob Storage, Azure Monitor

Nice to Have

NVIDIA Jetson edge device deployment experience (JetPack/aarch64)

Real-time video processing (OpenCV, GStreamer, RTSP)

Dataset versioning tools (DVC or similar)

WebRTC or WebSocket-based streaming experience

Exposure to Docker / Kubernetes for model serving at scale

Your next step

Have your CV and examples of relevant work ready.
Check the listed location, eligibility and core experience before starting.
Ask the employer about the salary range before committing time to the process.

Complete your application on ignatiuz.zohorecruit.in. The employer’s form will show what is required.

Already applied? Track this application

Source & posting history

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Source notes

Source excerpts

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Pay

No pay amount identified in the saved description.

Location & working pattern

Indore, India

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

Open source at ignatiuz.zohorecruit.in ↗Read the full description ↓

Status in our records: Active
First seen by us: Jul 8, 2026
Recorded sightings: 33
Last seen by us: Oct 10, 2026

These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.

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MLOps Engineer