Senior Gen AI Engineer

About Turing

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage.

Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. Learn more at www.turing.com

Location

Remote / Hybrid (HQ visits as needed)

Experience

5 + years in software engineering; 2 + years in GenAI

Engagement

Full-Time, Permanent

ABOUT THE ROLE

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.

KEY RESPONSIBILITIES

Knowledge Graph Engineering

• 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.

Agentic AI Systems

• 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.

Context Enrichment & Data Fusion

• 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.

MLOps & Production Readiness

• 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.

REQUIRED SKILLS & EXPERIENCE

• 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-ran