Sr. AI Engineer - Data Pipelines & Context Systems
Tasks
- Build incremental indexing and vector updates with lineage
- Build secure MCP API tools and services
- Build testing harnesses for prompts retrieval and tool calls
- Create evaluation datasets and regression checks
- Define chunking embedding tagging and provenance strategies
- Design RAG retrieval systems with hybrid search
- Design and implement production data pipelines
- Develop admin and debugging tools for operators
- Implement OAuth SSO and RBAC ABAC access controls
- Implement review approval rollback and exception workflows
- Ingest normalize enrich synchronize structured and unstructured data
- Model context boundaries and access layers
- Monitor freshness data quality duplicates and hallucination risk
- Translate business workflows into governed AI capabilities
Perks/Benefits
Skills/Tech-stack
ABAC | API Design | Asynchronous programming | Auditability | Chunking | Data Governance | Data Ingestion | Data Modeling | Data Privacy | Data Transformation | Data synchronization | Docker | Embeddings | Evaluation | Hybrid search | Infrastructure as Code | Kubernetes | LLM Tool Calling | MCP | Metadata filtering | Node.js | OAuth | Observability | OpenSearch | PGVector | Pinecone | Provenance | Python | RAG | RBAC | Regression testing | Reranking | SSO | Schema Validation | Structured outputs | Tool-Calling | TypeScript | Vector Databases | Vector Search | “as-code”
Education
N/A
Roles
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