MLOps Engineer

We are looking for a skilled MLOps Engineer to design, develop, deploy, automate, and maintain scalable Machine Learning (ML) and AI solutions in production environments. The ideal candidate should have hands-on experience with ML pipelines, CI/CD automation, cloud infrastructure, containerization, model deployment, monitoring, and model lifecycle management.

Key Skills:

Python, Bash, SQL

Scikit-learn, TensorFlow, PyTorch

FastAPI / Flask

MLOps Tools

MLflow

Kubeflow

Apache Airflow

DVC

Weights & Biases

Feature Stores

DevOps & CI/CD

Docker

Kubernetes

Jenkins

GitHub Actions / GitLab CI

Git, Linux

Cloud Platforms

AWS (SageMaker, EKS, S3, EC2)

Azure Machine Learning

Google Vertex AI

Infrastructure & Monitoring

Terraform

Prometheus

Grafana

ELK Stack

Evidently AI

Machine Learning Knowledge

Model training and deployment

Model evaluation and optimization

Model versioning and reproducibility

Data drift and model monitoring

Automated model retraining

Batch and real-time inference

Preferred Qualifications

Experience with LLMOps and Generative AI deployment.

Knowledge of RAG pipelines, vector databases, and embedding models.

Experience deploying LLM applications using Hugging Face, LangChain, or similar frameworks.

Knowledge of model quantization, optimization, and GPU-based inference.

Familiarity with distributed training and scalable ML architectures.

Experience with security, governance, and compliance in production ML systems.