I
AI Engineer (GCP)
Insight Global
3 hours ago
Full-time
On-site
Atlanta, Georgia, United States
AI Engineer
Required Skills & Experience: • 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. • Cloud experience (Azure, AWS, or GCP) with AI/ML services (Azure OpenAI, SageMaker, Vertex AI). • Bias toward action — you ship PoCs in days, not months, and you learn new stacks quickly. Nice to Have Skills & Experience: BigQuery Conversational Analytics(BQCA) Job Description: 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. What You'll Do: • 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. Pay: 45-55/HR based on experience
Required Skills & Experience: • 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. • Cloud experience (Azure, AWS, or GCP) with AI/ML services (Azure OpenAI, SageMaker, Vertex AI). • Bias toward action — you ship PoCs in days, not months, and you learn new stacks quickly. Nice to Have Skills & Experience: BigQuery Conversational Analytics(BQCA) Job Description: 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. What You'll Do: • 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. Pay: 45-55/HR based on experience