Job Description

Job Summary

We are looking for an MLOps Engineer to design, build, and operate scalable infrastructure and pipelines for deploying and managing machine learning models in production. The role bridges Data Science, Machine Learning Engineering, DevOps, and Cloud Engineering, ensuring models can move reliably from experimentation to production with strong automation, monitoring, governance, and reproducibility.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines covering model training, validation, deployment, monitoring, and retraining.
• Build automated CI/CD/CT pipelines for machine learning applications.
• Deploy and manage ML models as scalable batch, real-time, or streaming services.
• Containerize ML applications using Docker and orchestrate workloads using Kubernetes.
• Build and maintain cloud-based ML infrastructure on AWS, Azure, or GCP.
• Implement experiment tracking, model versioning, model registries, and artifact management.
• Establish monitoring for model performance, data drift, concept drift, latency, infrastructure health, and failures.
• Automate infrastructure provisioning using Infrastructure as Code tools such as Terraform.
• Work closely with Data Scientists to productionize models and improve reproducibility of ML experiments.
• Collaborate with DevOps, Data Engineering, Security, and application teams to integrate ML solutions into enterprise platforms.
• Implement security, access control, auditability, and governance practices across the ML lifecycle.
• Troubleshoot production ML pipelines and continuously improve platform reliability, scalability, and cost efficiency.
• Develop reusable MLOps frameworks, templates, and engineering standards.

Skill Requirements

• Strong programming skills in Python and scripting experience with Bash/Shell.
• Hands-on experience with Docker and Kubernetes.
• Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
• Experience building CI/CD pipelines using tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
• Hands-on experience with MLOps tools/platforms such as MLflow, Kubeflow, Azure Machine Learning, AWS SageMaker, or Vertex AI.
• Good understanding of the machine learning lifecycle, including training, validation, deployment, inference, monitoring, and retraining.
• Experience with Git and software engineering best practices.
• Knowledge of Infrastructure as Code using Terraform, CloudFormation, or equivalent technologies.
• Experience with monitoring and observability platforms such as Prometheus, Grafana, ELK, or cloud-native monitoring services.
• Understanding of REST APIs, microservices, and distributed systems.
• Familiarity with data pipelines, databases, object storage, and feature engineering workflows.

Other Requirements

• Experience with orchestration platforms such as Apache Airflow, Kubeflow Pipelines, or Argo Workflows.
• Knowledge of feature stores such as Feast or cloud-native feature-store solutions.
• Experience implementing automated model/data drift detection and retraining workflows.
• Exposure to Generative AI/LLMOps, including LLM deployment, evaluation, prompt/version management, vector databases, and RAG pipelines.
• Understanding of GPU-based workloads and distributed model training/inference.
• Knowledge of enterprise security, compliance, and responsible AI practices.