Primary Focus: Master Coordinator, domain agents, semantic retrieval, knowledge graphs, data foundations, and agent quality
Backend Engineering: Advanced Python; agent services; multi-step workflows; orchestration state; tool invocation; fallback handling; reusable agent patterns
API and Integration: Standard agent and tool contracts; agent registration; deterministic and LLM-assisted routing; multi-domain execution; model and platform adapters
Data Engineering: Advanced SQL and BigQuery; source discovery; data-gap analysis; data dictionaries; business-key validation; reconciliation; quality, freshness, lineage, and source-of-truth assessment
AI and Retrieval: Deep RAG expertise; retrieval and reranking; grounding; prompt/context assembly; citation support; hallucination reduction; cross-domain synthesis
Knowledge Graph: RDF, SPARQL, ontology modeling, SHACL, Stardog, virtual graphs, relational-to-semantic mappings, semantic versioning, and graph promotion
Cloud and Infrastructure: GCP, GKE, BigQuery, GCS, agent-runtime deployment, semantic-platform connectivity, environment configuration, and secure service identities
CI/CD and Release: Agent and knowledge-graph CI/CD; automated evaluation gates; versioned prompts, tools, mappings, ontologies, queries, and deployment definitions
Testing and Quality: Agent evaluation for accuracy, relevance, groundedness, completeness, hallucination, latency, and cost; benchmark scenarios and gold-answer datasets
Observability and Operations: End-to-end tracing across coordinator, agent, tool, semantic, and data layers; Langfuse or equivalent LLM observability; Grafana reporting and model comparisons
Security and Governance: Identity-aware retrieval; least-privilege data access; cross-domain guardrails; trace and prompt-data protection; source attribution and complete auditability
Tech Lead Expectations: Own orchestration, semantic, and data-foundation design; remain hands-on; create reusable patterns; mentor engineers; drive evaluation and production readiness