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Diamond Layer Tech Lead Hybrid

Tektree Systems Inc.Mahwah, NJ🇺🇸United StatesPosted Oct 1, 2026

Why This Role Stands Out

This hybrid Tech Lead role offers a unique opportunity to architect and advance cutting-edge AI solutions, focusing on knowledge graphs and agent orchestration within a reputable company. You'll thrive here if you possess deep expertise in Python, SQL, and AI/ML, with a passion for building robust, scalable systems and collaborating within a dynamic team environment. Apply now to shape the future of intelligent systems and accelerate your career growth.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Mahwah, NJ, United States
Posted
17 hours ago
SQLAssemblyBigQueryGoogle CloudGrafanaLLMPythonReconciliation

Job Description

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: Google Cloud Platform, 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


Years of Experience:    15.00 Years of Experience

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