AI Engineer (GCP)

About the role

We're looking for a hands-on AI Engineer to join our Innovation team, where the mission is to prove out what's next fast. You'll spend your time exploring emerging GenAI patterns, graph-driven architectures, and agentic workflows, translating fuzzy ideas into working proofs of concept that inform how the business adopts AI at scale. This is a builder's seat: you'll prototype, iterate, and demo not maintain legacy pipelines.

Responsibilities

• Design and ship rapid PoCs across GenAI use cases: RAG, agentic workflows, LLM orchestration, semantic search, and knowledge graphs.
• Apply graph theory and graph-based data structures (Neo4j, NetworkX, GraphRAG, LangGraph) to model relationships, enable multi-hop reasoning, and power retrieval systems.
• Build end-to-end AI pipelines integrating LLMs (OpenAI, Anthropic, Azure OpenAI, Hugging Face) with vector stores, embeddings, and enterprise data.
• Write clean, performant SQL to shape training/eval datasets and integrate structured data into GenAI workflows.
• Evaluate emerging tools and frameworks (LangChain, LangGraph, LlamaIndex, MCP, Pinecone, Weaviate, FAISS) and recommend what earns a spot in the stack.
• Partner with product, data science, and business stakeholders to translate ambiguous problems into demoable prototypes within tight sprints.
• Document findings, benchmarks, and trade-offs so successful PoCs can hand off cleanly to production teams.

Qualifications

• 3+ years of engineering experience with at least 1+ years hands-on in AI/ML or GenAI.
• Strong Python; production comfort with LangChain, LangGraph, or equivalent LLM orchestration frameworks.
• Demonstrated experience with Generative AI: RAG, prompt engineering, embeddings, fine-tuning (LoRA/PEFT), agentic systems.
• Working knowledge of graph theory, graph databases, graph algorithms, or GraphRAG-style retrieval.
• Solid SQL and comfort working with structured + unstructured data.
• GCP Cloud experience, including GKE, Big Query, Vertex AI, etc.