Description

Define end-to-end AI/GenAI architecture for enterprise-grade applications, driving platform strategy and technical governance.

This role is on-site.

Responsibilities

• Architect scalable LLM/SLM solutions, multi-agent systems, and RAG pipelines using Google Cloud (Vertex AI, GKE, Cloud Run).
• Design MLOps pipelines for training, deployment, and monitoring, establishing CI/CD strategies using GitHub Actions or GitLab CI.
• Lead adoption of Google AI ecosystem tools, including Vertex AI, Agent Development Kit (ADK), and Workspace integrations.
• Define backend architecture using FastAPI or Node.js, managing API security and frontend integration for AI-driven applications.
• Establish AI governance frameworks for ethics, bias mitigation, model lifecycle monitoring, and compliance.

Required Skills

• 12+ years of software engineering experience, including 7+ years in AI/ML with a focus on Generative AI and LLMs.
• Deep expertise in Google Cloud AI stack: Vertex AI, Gemini, ADK, BigQuery, and Vector Databases (ChromaDB).
• Strong proficiency in Python and familiarity with Node.js for backend development.
• Hands-on experience with MLOps, CI/CD, and cloud-native architecture (GCP, Kubernetes).
• Architecting multi-agent systems and complex workflows using LangChain, LangGraph, LlamaIndex, or Semantic Kernel.
• Implementing model optimization strategies (LoRA, QLoRA) and evaluation frameworks (HELM, lm-eval, RAGAS).
• Experience with observability tools such as LangSmith, MLflow, or Weights & Biases.

Preferred Skills

• Google Cloud Certifications (Professional ML Engineer / Cloud Architect).
• Experience contributing to open-source AI/ML projects or building enterprise AI platforms.

Key Skills

Education

Bachelor's degree