Senior AI Engineer — Agentic Systems and Knowledge Graphs

The Mission:
We are building a sovereign company-wide AI fabric. This system ingests every document ticket transaction and data point across our portfolio (Veralogix Bioniq Innovata Treadstone Aiguille) into a unified retrieval property-graph and agentic layer. It provides natural-language grounded and traceable insights in real-time.

You will own the end-to-end architecture as part of a small high-impact engineering team focusing on on-prem-first deployment and seamless integration with our spatio-temporal platform.

Responsibilities:

• Design and implement a chunking strategy for heterogeneous content: PDFs scanned docs transcripts tables code and ERP records.

• Engineer hybrid search (BM25 dense late-interaction) reranking and query routing.

• Architect and operate the vector database infrastructure (pgvector Qdrant or equivalent).

• Design and implement the ontology for the corporate knowledge graph (Memgraph Neo4j or KuzuDB).

• Execute entity resolution across systems like Odoo Aleph Hoover Elasticsearch Nextcloud and telemetry.

• Build GraphRAG patterns that solve complex multi-hop queries more effectively than naïve vector retrieval.

• Author and manage a fleet of MCP servers to expose internal systems (Odoo Hoover Aleph Memgraph Elasticsearch etc.) to the agent layer.

• Implement robust scoping authentication auditing and rate limiting.

• Build and deploy multi-agent systems using frameworks like LangGraph Pydantic AI DSPy or BAML.

• Implement planner/researcher/executor/verifier patterns with structured outputs error recovery and long-running stateful workflows (e.g. using Temporal).

• Build an evaluation harness from scratch with golden sets and RAGAS-style metrics.

• Establish a rigorous LLM-as-judge methodology to block deploys on regression.

• Set up observability (Langfuse/Phoenix) cost/latency dashboards and a version-controlled prompt management system.

• Production ML/AI: 5 years of experience with 2 years specifically on LLM-era systems (RAG agents fine-tuning).

• Production RAG: Designed and operated a system over heterogeneous content. Can defend decisions on chunking hybrid retrieval and reranking from first principles.

• Vector Databases: Deep operational knowledge of pgvector Qdrant Weaviate or LanceDB. Has opinions on HNSW vs. IVF quantization hybrid indices and index rebuild strategies.

• Property Graphs: Production experience with Memgraph Neo4j or KuzuDB. Fluent in Cypher has designed an ontology and built production GraphRAG retrievers.

• Agentic Tooling: Authored MCP servers or has equivalent depth with production tool-use design (OpenAI function calling Bedrock agents).

• Agentic Frameworks: Production experience with LangGraph Pydantic AI DSPy or BAML. Has shipped multi-agent systems with real tool calls and recovery logic.

• Evaluation Discipline: Has built an eval harness from scratch and can defend the metrics and regression rates of their prompt changes.

• Information Extraction: Proficient with NER/RE models and structured-output pipelines (Pydantic/JSON schema/BAML) beyond simple prompting.

• Python Mastery: Operating at a staff-engineer level. Can read research papers and implement them.

• Model Fine-Tuning: Experience with LoRA/QLoRA or full fine-tuning of open-weight models.

• Advanced Retrieval: Production experience with ColBERT SPLADE or similar late-interaction/ sparse-dense hybrid systems.

• Novel RAG Architectures: Shipped systems like GraphRAG (Microsoft) Contextual Retrieval (Anthropic) or HippoRAG.

• Entity Resolution at Scale: Experience with Splink Zingg dedupe or similar libraries.

• Local Model Serving: Skilled with vLLM TensorRT-LLM SGLang or .

• Spatio-Temporal RAG: Experience with natural-language querying over PostGIS TimescaleDB.

• OSINT Workflows: Integrated with tools like Maltego SpiderFoot Aleph or Hoover.

• Agentic Coding: Regular user of Claude Code or similar tools as a default working mode.

• Open Source: Contributions to BAML DSPy LangGraph or comparable libraries.