Senior Gen AI Engineer

Turing

14h•Hybrid

About The Position

We are looking for a talented Sr. GenAI Engineer who sits at the intersection of knowledge engineering, agentic AI, and data intelligence. In this role you will design and operate AI agents that traverse, reason over, and enrich large-scale knowledge graphs — then extend that context dynamically using live data sources such as the web, enterprise APIs, and structured databases. The ideal candidate is deeply comfortable with graph data models, LLM orchestration frameworks, and retrieval-augmented pipelines. Bonus points if you have experience working in trade-craft or intelligence-adjacent environments where provenance, precision, and adversarial robustness are non-negotiable.

Requirements

5 + years of professional software engineering with strong Python (or Java / Kotlin) proficiency.
Hands-on production experience with at least one major graph database — Neo4j, Amazon Neptune, TigerGraph, or comparable.
Demonstrated knowledge of graph query languages like Cypher, SPARQL, or Gremlin — at production query complexity.
Direct experience building LLM-powered agents or pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel.
Solid understanding of RAG architectures: chunking strategies, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, and re-ranking.
Familiarity with prompt engineering, few-shot learning, and LLM evaluation techniques.
Experience integrating external data sources via APIs, web scraping (Playwright / Scrapy), or streaming pipelines (Kafka / Kinesis).
Working knowledge of containerisation (Docker, Kubernetes) and CI/CD pipelines.
Familiarity with graph export formats - at least one GraphML, RDF/OWL, or JSON-LD
Experience integrating GNN-derived features into vector stores or RAG pipelines

Nice To Haves

Advanced degree (MS / PhD) in Computer Science, Information Science, Computational Linguistics, or a related field.
Experience in intelligence, defence, or trade-craft environments — working with OSINT, link analysis, entity disambiguation, or signals intelligence data.
Understanding of access-control models for sensitive graph data (need-to-know, compartmentalisation, provenance labelling).
Familiarity with knowledge representation standards like OWL, SHACL, RDF-star, JSON-LD, W3C PROV.
Experience with fine-tuning or instruction-tuning open-source LLMs (Llama, Mistral, Falcon) for domain-specific tasks.
Background in network-analysis algorithms: centrality, community detection, path-finding, anomaly detection on graphs.
Contributions to open-source graph or GenAI projects; published research or technical blog presence.
Active or adjudicatable security clearance (Secret or above) — strongly preferred for trade-craft assignments.
Trade-Craft Experience — A Significant Plus: Candidates with backgrounds in intelligence analysis, signals intelligence, law enforcement data fusion, or related trade-craft disciplines are strongly encouraged to apply. Understanding of link analysis, entity disambiguation under adversarial conditions, handling classified or compartmentalized data, and mission-driven product constraints will set you apart.

Responsibilities

Design, build and maintain large-scale property graphs and RDF triplestores (Neo4j, Amazon Neptune, Stardog, or equivalent).
Develop and govern ontologies, taxonomies, and entity-relationship schemas that reflect real-world domain semantics.
Implement graph ingestion pipelines that extract, transform, and link entities from structured, semi-structured, and unstructured data.
Optimise graph traversal queries (Cypher, SPARQL, Gremlin) for sub-second response at production scale.
Train and deploy graph neural networks (GNNs) for node classification, link prediction, and subgraph retrieval - Maintain model retraining workflows triggered by graph drift or coverage degradation.
Architect and implement autonomous agents that plan multi-step reasoning chains over knowledge graph data using LLMs (GPT-4o, Claude, Gemini, or open-source equivalents).
Build graph-aware Retrieval-Augmented Generation (RAG) pipelines that blend structured graph context with unstructured document retrieval.
Design tool-use and function-calling layers so agents can query live data sources — web search, REST/GraphQL APIs, relational databases — to extend or verify graph knowledge.
Implement agent memory, reflection, and self-correction loops to improve reliability over multi-hop tasks.
Integrate web scraping, news feeds, and open-source intelligence (OSINT) sources to keep the knowledge graph current.
Build entity resolution and deduplication components that merge data from heterogeneous sources into a consistent graph.
Develop confidence-scoring and provenance-tracking mechanisms so downstream consumers understand the reliability of any piece of context.
Package agents as scalable microservices; instruments with observability tooling (tracing, latency, token cost).
Collaborate with platform engineers to deploy workloads on cloud-native infrastructure (AWS / GCP / Azure).
Maintain evaluation harnesses that measure agent accuracy, hallucination rate, and graph coverage over time.