MLOps Engineer

Job Description

Roles & Responsibilities :

– ML Pipelines : Design and build CI/CD pipelines specifically for ML workflows (training triggers, model versioning, testing) using tools like Jenkins, Bitbucket, or GitHub Actions.

– Orchestration : Deploy, configure, and optimize Kubernetes clusters to support containerized deep learning applications (managing GPU resources, node scaling).

– Model Serving : Work with Data Scientists to containerize and deploy PyTorch models using Docker and serving frameworks (KServe, Nvidia Triton Inference Server).

– Infrastructure : Manage cloud infrastructure (AWS) for data processing and model storage (S3, ECR, IAM).

– GitOps : Implement GitOps practices to manage the lifecycle of both infrastructure and ML configurations.

– Monitoring : Implement monitoring for both system health (CPU/Memory) and Model Drift/Performance using tools like Prometheus, Grafana, or ELK.

– Automation : Automate repetitive tasks related to dataset management and environment setup using Python.

Qualification :

– 1 – 3 years of relevant experience as MLEngineer, MLOps, or Platform Engineering roles.

– Mandatory : Functional understanding of Machine Learning/Deep Learning concepts and the PyTorch framework.

– Mandatory : Prior experience working with Kubernetes and CI/CD in a production environment.

– Bachelors degree in Computer Science, IT, or a related field; non-IT degrees with relevant experience are also acceptable

Must-have Skills :

– Core MLOps : Practical experience deploying ML/DL models in production systems. You understand the difference between deploying a web app and deploying a deep learning model.

– Kubernetes : Strong hands-on experience with K8s (deployments, services, ingress) and preferably experience scheduling GPU workloads.

– CI/CD & GitOps : Proficiency in building pipelines (Jenkins/Bitbucket) and understanding GitOps workflows (ArgoCD/Flux).

– ML Fundamentals : Working knowledge of PyTorch and Python. You should be able to read model code, understand training/inference loops, optimize pytorch models and debug environment issues (CUDA, dependencies).

– Containerization : Expert-level Docker skills (multi-stage builds, reducing image sizes for large ML dependencies).

– Cloud : Experience with AWS services (EC2, S3, ECR).

– Linux/Scripting : Strong command of Linux internals and shell scripting.

Good-to-have :

– Experience with ML workflow tools like KServe, Triton Inference Server and MLflow.

– Experience profiling and optimizing PyTorch models for production inference on accelerator platforms such as NVIDIA GPUs, TPUs, and AWS Inferentia

– Background in processing Geospatial or Remote Sensing data.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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