AI/ML Engineer
Tasks
- Architect multi environment deployment pipelines
- Automate CI CD for model deployment
- Build end-to-end ML pipelines
- Design feature engineering inference pipelines
- Ensure reliability, scalability, and security
- Evaluate models
- Implement containerization for ML workloads
- Implement security and governance controls
- Ingest data
- Maintain infrastructure as code for cloud resources
- Maintain reproducible environments
- Monitor ML solutions in production
- Perform feature engineering
- Resolve production issues
- Serve ML solutions
- Train models
Perks/Benefits
- Drug test
- EPassi
- Gym access
- Hobby club activities
- Pre employment security clearance
- Sickness fund insurance
- Travel benefits
Skills/Tech-stack
AWS | Agentic Orchestration | Azure | Azure DevOps | CI/CD | CI/CD automation | Cloud Governance | Cloud Networking | Cloud Security | Docker | Feature Engineering | Fine Tuning | GenAI | GitHub Actions | Infrastructure as Code | JavaScript | Kubernetes | LLM Fine-tuning | MLOps | Machine Learning | Machine Learning Pipelines | Model Evaluation | Model Serving | Model Training | Python | RAG | Vector Search | “as-code”
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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