Senior Engineer - AI
Tasks
- Apply enterprise security governance and RBAC
- Automate model deployment
- Build production applications
- Create feature stores
- Deliver Agentic AI and GenAI workloads
- Design AI solutions
- Design LLM evaluation and test frameworks
- Develop RAG pipelines
- Enable hybrid retrieval architectures
- Implement CI/CD monitoring and observability
- Implement MLOps with Azure ML and Databricks
- Improve model latency and cost
- Integrate with Azure data services and APIs
- Lead workshops mentor engineers and present to stakeholders
- Manage model registry
- Monitor drift bias and quality
- Optimize prompts
- Produce HLD and LLD
- Provision infrastructure as code
- Set up telemetry and model monitoring
- Track experiments
Perks/Benefits
- Career pathways
- Inclusion
- Learning opportunities
- Remote/hybrid work flexibility
- Supportive workplace
- Work from home options
Skills/Tech-stack
AI Search | AKS | Azure | Azure AI | Azure AI Search | Azure Data | Azure Data Lake | Azure Data Lake Storage | Azure DevOps | Azure ML | Azure Monitor | Bicep | CI/CD | Data Lake Storage | Databricks | Delta Lake | Docker | Entra | Event Hubs | Experiment tracking | Feature Store | GenAI | GitHub Actions | Grafana | IaC | Key Vault | LLM | Langchain | MLOps | Microsoft Fabric | Model Monitoring | Model Registry | PGVector | Prometheus | Prompt engineering | Python | RAG | RBAC | Redis | Semantic Kernel | Synapse | Terraform | Vector Databases
Education
N/A
Roles
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