Principal Architect, AI/ML
New
Fully remote work environment within the United States.Full-TimePrincipal
Salary not disclosed
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Job Details
- Required Skills
- GCPMachine LearningPyTorchGenerative AILangChain
Requirements
- Master’s degree in Computer Science, Mathematics, Engineering, or a related technical field, or equivalent practical experience.
- Extensive experience in senior or principal architecture roles delivering production-grade AI/ML systems at scale.
- Strong hands-on experience with at least one major cloud platform (Google Cloud strongly preferred; AWS or Azure acceptable).
- Deep expertise in LLM optimization techniques including fine-tuning (e.g., LoRA), quantization, and efficient inference (e.g., vLLM, TensorRT-LLM).
- Experience with modern ML frameworks such as PyTorch, JAX, or PyTorch/XLA for training and deployment.
- Proven ability to design and implement agentic AI systems using frameworks like LangChain, LangGraph, or similar tools.
- Strong understanding of secure, private, and data-sovereign AI architecture patterns.
- Familiarity with observability and evaluation tools for LLM systems (e.g., LangSmith, Langfuse, Vertex AI Evaluation).
- Excellent communication and stakeholder management skills, with the ability to translate technical depth into business impact.
- Passion for mentoring, innovation, and staying at the forefront of AI/ML advancements.
Responsibilities
- Serve as the senior technical authority on AI/ML architecture, guiding the design and implementation of advanced machine learning and generative AI systems.
- Lead pre-sales and solution design efforts, partnering with business development teams to scope opportunities and present technical solutions to clients.
- Architect end-to-end AI systems, including model training, fine-tuning, optimization, deployment, and production-scale serving.
- Design secure, privacy-compliant, and data-sovereign AI solutions aligned with regulatory requirements such as GDPR.
- Guide the selection and application of cloud-native technologies, with emphasis on Google Cloud and large-scale AI infrastructure.
- Lead the design of agentic systems and workflows using modern frameworks such as LangGraph, LangChain, and similar tooling.
- Optimize model performance and cost efficiency through techniques such as quantization, pruning, and advanced serving strategies.
- Collaborate with cloud and data architects to ensure cohesive, scalable end-to-end solutions for enterprise clients.
- Provide technical mentorship to internal teams and clients, establishing best practices in high-performance AI engineering.
- Act as a thought leader through external contributions such as technical writing, presentations, and industry engagement.
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