Principal Architect: AI & GCP Agentic Stack
New
C
ConcentrixAI architecture
Remote in USAFull-TimePrincipal
SalaryThe base salary for this position is $155,000 - $220,000, plus incentives that align with individual and company performance.
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Job Details
- Experience
- 12+ years in technical architecture with a focus on AI, Data, Integration, and consultative leadership
- Required Skills
- PythonPrompt EngineeringMLOps
Requirements
- Bring 12+ years in technical architecture focused on AI, data, integration, and consultative leadership.
- Have expert-level knowledge of Gemini Enterprise (formerly Agent space), Vertex AI, and BigQuery ML.
- Have customer-facing experience and the ability to influence CXO stakeholders and lead technical solutioning for AI-driven business transformations.
- Have experience in prompt design and engineering, ideally with Gemini.
- Have experience tuning LLM-based systems for specific behaviors and safety constraints.
- Have a deep understanding of Python-based AI orchestration, such as LangGraph or ADK.
- Google Professional Machine Learning Engineer certification is desired.
- Experience with high-performance computing (HPU/TPU) and specialized AI hardware on GCP is desired.
Responsibilities
- Partner with sales to lead technical discovery, perform value-engineering, and design tailored AI architectures for client business challenges.
- Design end-to-end architectures for complex AI systems, including RAG and multi-agent systems.
- Provide technical governance during implementation to ensure delivery aligns with architectural intent.
- Advise clients and Google's technical field teams, coordinating joint-innovation pursuits and aligning with Gemini and Vertex AI roadmaps.
- Lead configuration of the Gemini Enterprise platform and architect agentic systems with prompt engineering, behavioral tuning, and human-in-the-loop oversight.
- Develop and optimize prompt strategies for the Gemini ecosystem through iterative tuning and system instructions.
- Define MLOps and data orchestration standards for secure integration with corporate data estates and GCP best practices.
- Review AI project architectures for adherence to Google Cloud best practices.
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