GenAI Architect

We are seeking an experienced GenAI Architect to design and implement scalable, secure, enterprise-grade Generative AI solutions. The ideal candidate will have strong expertise in AI architecture, LLMs, RAG, Agentic AI, and Google Cloud Platform (GCP), along with a solid software engineering and enterprise integration background.

Key Responsibilities

• Design end-to-end GenAI architectures for enterprise products and applications.
• Develop and integrate LLM-powered solutions using RAG, embeddings, vector databases, and AI agents.
• Establish best practices for prompt engineering, model orchestration, inference, and AI application development.
• Integrate GenAI capabilities into existing enterprise platforms using APIs and microservices.
• Design scalable AI workflows with model routing, fallback mechanisms, and performance optimization.
• Implement MLOps practices for model deployment, monitoring, versioning, and lifecycle management.
• Ensure AI security, data privacy, governance, and responsible AI compliance.
• Collaborate with product managers, data engineers, and development teams to translate business requirements into technical solutions.

Required Qualifications

• 8+ years of experience in software architecture, software engineering, ML engineering, or platform engineering.
• 2+ years of hands‑on experience with AI/ML and Generative AI technologies.
• Strong programming skills in Python, Java, or similar languages.
• Expertise in LLMs, prompt engineering, RAG, embeddings, and Agentic AI frameworks.
• Experience designing enterprise-grade AI architectures and integrating AI into production applications.
• Strong experience with GCP and cloud‑native application development.
• Knowledge of vector databases, data pipelines, document processing, and knowledge management.
• Experience with Kubernetes, Docker, CI/CD, and MLOps practices.
• Understanding of AI security, guardrails, responsible AI, and enterprise governance.

Preferred Skills

• Experience with foundation models such as GPT, Claude, or LLaMA.
• Knowledge of multi‑agent systems, workflow orchestration, and knowledge graphs.
• Experience with Vertex AI, Big Query, and other GCP services.
• Familiarity with model evaluation, inference optimization, monitoring, and cost management.
• Experience with GDPR, data privacy, and regulated enterprise environments.
• Knowledge of A/B testing, feature flags, and AI product lifecycle management.