AI Architect

Job not on LinkedIn

🕒 October 21

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 AI Engineer

👻 Ghost score 15%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Insight Softmax Consulting

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2016

💼 Consulting

🤖 Artificial Intelligence

🤝 B2B

Consulting • Artificial Intelligence • B2B

Insight Softmax Consulting is a data-focused consulting firm that combines data science, software engineering, and high-performance computing to solve business and research problems end-to-end. They emphasize defining business problems first and then delivering solutions in data analytics, AI/ML (including generative AI and LLM training), and last-mile HPC infrastructure (cloud-native HPC, accelerated computing for CFD and advanced scientific workloads). The company also offers open-source expertise and services to make powerful compute and AI capabilities accessible to enterprises and research organizations. Offices are listed in San Francisco, USA and Zürich, Switzerland.

📋 Description

• Lead technical discovery with customers and translate business goals into AI platform architectures, phased roadmaps and effort estimates • Design inference serving platforms on Kubernetes and bare metal, including model serving, GPU scheduling and partitioning, autoscaling, multi-tenancy and capacity planning • Design AI gateway and routing layers with tenant-aware routing, model pools, budgets and quotas, rate limiting, observability and multi-site failover • Define reference architectures for multi-tenant GPU clouds, sovereign and air-gapped deployments, and hybrid cloud and on-premises estates • Advise on AI usage governance, including cost visibility, token accounting, audit, data handling and security posture for LLM services • Evaluate and recommend build, buy or compose decisions across open-source and commercial AI infrastructure components • Prototype, validate assumptions in a lab and review implementations delivered by AI and platform engineers • Own the technical quality of project outcomes • Represent Insight Softmax in customer architecture reviews, partner enablement sessions and executive readouts • Support pre-sales scoping and estimates • Reserve time each week for pre-sales support, internal platform work and mentoring

🎯 Requirements

• At least 8 years in infrastructure, platform or ML engineering • At least 3 years designing and operating production AI or ML platforms at scale • Hands-on experience deploying LLM inference in production, including tensor and pipeline parallelism, batching, caching and performance tuning • Deep understanding of NVIDIA and AMD accelerators, drivers and runtimes, GPU scheduling on Kubernetes, high-speed interconnects and storage for AI workloads • Experience designing AI gateway or API management layers • Strong Kubernetes and Linux skills; able to read and debug the stack from the scheduler down to the node • Deep knowledge of at least one of AWS, GCP, or Azure; preferably AWS • Track record of leading architecture engagements with enterprise customers, including writing proposals, estimates and architecture documents that engineers can execute against • Clear written and spoken communication with both engineers and executives • 3 references will be required • Optional: Experience at an AI lab, AI-native company, neocloud, or building platform products for them • Optional: Multi-tenant GPU cloud design, Slurm or other batch schedulers, RAG, agentic systems, model evaluation, fine-tuning pipelines, air-gapped or FIPS deployments, Spectro Cloud Palette and PaletteAI, Run:ai, observability for AI services • Bonus: Python, Go or Rust; software engineering background; published talks, blog posts or open source contributions in AI infrastructure; using Linux as a primary desktop workstation environment

🏖️ Benefits

• Remote work environment • Occasional travel to customer sites in the US for architecture workshops, load tests and go-live reviews

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