Principal Architect, AI/ML
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 22h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 151d ago
The date the source published, not the day we noticed it (2026-04-16). Last seen at its source 2h ago.
Is it remote?
Remote
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Not stated
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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lever employer's own board first seen 26d ago · last seen 2h ago
The listing
What you will do...
- Serve as Zencore’s senior-most technical authority on the practical application of advanced artificial intelligence and machine learning.
- Partner with the sales and business development teams in a pre-sales capacity to scope opportunities, design solutions for proposals, and act as the senior technical voice in client pitches.
- Lead the architecture and design of sophisticated, secure, and scalable AI solutions for our clients, moving beyond standard API integrations to create genuine competitive advantages.
- Collaborate closely with Cloud & Data Architects to guarantee the design and deployment of comprehensive client solutions.
- Address the growing demand for private, data-sovereign AI by designing systems that meet strict GDPR and data privacy requirements. Strive for model explainability and bias mitigation, ensuring solutions adhere to ethical standards and safety guardrails.
- Architect solutions for hosting, fine-tuning, and optimizing both proprietary (e.g., Gemini, Claude) and open-source (e.g., Llama, Mistral) models on hyperscaler platforms.
- Lead clients in selecting optimal cloud-native technologies, prioritizing Google Cloud solutions for deploying and scaling production-grade agentic systems.
- Guide and mentor customers and Zencore's engineering teams on advanced topics, establishing best practices for high-performance training (PyTorch, JAX, TPUs), efficient model serving (vLLM), and complex agentic systems (LangGraph, Langchain, Google ADK).
- Devise the financial architecture of AI solutions by performing ROI analysis and implementing cost-optimization strategies to ensure large-scale deployments remain economically sustainable for customers.
- Act as an external thought leader, contributing to the Zencore brand through blog posts, conference presentations, and community engagement.
- Act as a "player-coach," providing hands-on leadership and fostering a culture of deep technical excellence in AI/ML.
Who we need...
- Master’s degree in Computer Science, natural sciences, mathematics, or a related technical field, or equivalent practical experience in designing and delivering high-scale AI/ML systems.
- Extensive experience in a senior or principal architect role with a proven track record of designing and delivering complex, production-grade machine learning systems that have created measurable business value.
- Deep, hands-on architectural experience with at least one major cloud platform (GCP, AWS, or Azure) is required.
- Direct, hands-on experience with Google Cloud (Vertex AI, GKE, TPUs) is a significant plus.
- Proven expertise in LLM optimization, including techniques for quantization, pruning, efficient fine-tuning (e.g., LoRA), and high-performance serving (e.g., vLLM, TensorRT-LLM).
- Hands-on experience with high-performance ML frameworks (e.g., JAX, PyTorch/XLA) for training or fine-tuning large-scale models.
- Expertise in designing and deploying agentic workflows using both code-centric (e.g., LangGraph, LangChain, Google ADK) and low-code (e.g., Vertex AI Agent Builder, LangSmith Agent Builder) paradigms.
- A strong understanding of the architectural patterns required for building secure, private, and data-sovereign AI solutions.
- Experience with LLM observability and evaluation frameworks (e.g., LangSmith, LangFuse, Vertex AI Evaluation).
- Exceptional communication and stakeholder management skills, with the ability to articulate complex technical concepts and their business value to both technical and non-technical audiences.
- A passion for mentoring and a drive for continuous learning in the fast-evolving AI landscape.