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.