Senior/Principal Local LLM & Generative AI Platform Engineer
Tasks
- Build CI CD and model registries
- Build modular inference model gateway layer
- Create versioned evaluation datasets
- Deploy containerized services on Kubernetes
- Design RAG and enterprise search pipelines
- Design safe tool calling workflows
- Develop reusable APIs and tools
- Enforce source permissions and access controls
- Establish release gates and regression tests
- Implement model and agent observability
- Implement permission-aware retrieval
- Implement security network isolation encryption and retention
- Integrate platform with employee tools via SDKs and reference implementations
- Integrate with engineering workflows
- Optimize inference performance with batching and caching
- Own local LLM platform architecture
- Run offline and online evaluation
- Select and optimize open weight models
Perks/Benefits
- N/A
Skills/Tech-stack
Access Control | Audit Logging | Automated testing | Batching | C plus plus | CI/CD | Capacity Planning | Citations | Context Management | Data poisoning | Data poisoning defense | Disaster Recovery | Docker | Embeddings | Encryption | Enterprise Search | Git | Go | Hybrid retrieval | Incident Response | Infrastructure as Code | Injection Defense | Java | KV caching | Kubernetes | Lexical search | Linux | Linux systems | Metadata filtering | Monitoring | Network isolation | Observability | Parallelism | Permission Aware Retrieval | Prompt Safety | Prompt engineering | Prompt injection | Prompt injection defense | Python | Quantization | RAG | Reranking | Retrieval-Augmented Generation | Role-Based Access | Role-Based Access Control | SSO | Secrets management | Structured Output | Tokenization | Tool-Calling | Transformer Models | Vector Search | Vulnerability Management | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Regions
Countries
States
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