Job VC
AI Engineer
Technologies
Description
As an AI Engineer, you will be at the forefront of our innovation engine, architecting the next generation of intelligent agentic systems. This isn't just about prompt engineering; it’s about building scalable, production-ready Generative AI ecosystems that solve high-stakes business challenges.
Key Responsibilities
Architect & Deploy: Design end-to-end GenAI solutions, specialized AI agents, and high-performance RAG pipelines.
Lead Innovation: Rapidly build and validate Proof of Concepts (PoCs) to showcase the "art of the possible" to stakeholders.
Consult & Collaborate: Partner with cross-functional teams and interface directly with clients to translate complex needs into tailored AI strategies.
Research & Evaluate: Stay ahead of the curve by benchmarking emerging models, agentic frameworks, and dev kits.
The Profile
The Experience: 3+ years of industry-hardened experience in Applied AI or Machine Learning.
The Tech Stack: Expert-level command of agentic frameworks (LangGraph, Agno, Google ADK) and Vector Databases.
Cloud Fluency: Deep familiarity with at least one major Hyperscaler (GCP, AWS, or Azure).
Bonus Points: Proficiency with the Google Cloud AI stack (Vertex AI, Gemini), MLOps (Docker/K8s), and rigorous evaluation frameworks like RAGAs or LangSmith.
AI agent building: know-how
Key Responsibilities
Architect & Deploy: Design end-to-end GenAI solutions, specialized AI agents, and high-performance RAG pipelines.
Lead Innovation: Rapidly build and validate Proof of Concepts (PoCs) to showcase the "art of the possible" to stakeholders.
Consult & Collaborate: Partner with cross-functional teams and interface directly with clients to translate complex needs into tailored AI strategies.
Research & Evaluate: Stay ahead of the curve by benchmarking emerging models, agentic frameworks, and dev kits.
The Profile
The Experience: 3+ years of industry-hardened experience in Applied AI or Machine Learning.
The Tech Stack: Expert-level command of agentic frameworks (LangGraph, Agno, Google ADK) and Vector Databases.
Cloud Fluency: Deep familiarity with at least one major Hyperscaler (GCP, AWS, or Azure).
Bonus Points: Proficiency with the Google Cloud AI stack (Vertex AI, Gemini), MLOps (Docker/K8s), and rigorous evaluation frameworks like RAGAs or LangSmith.
AI agent building: know-how