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Best match for senior engineers who lead AI application platforms and LLMOps, not pure research roles. Multi-country listing suggests flexibility on geography; remote flag is not set in the job data, so confirm work arrangement with the employer.
As Lead AI Application Engineer (Infrastructure & LLMOps) at TechBiz Global, expect ownership of how LLM-backed apps are built, deployed, and run in production: platform reliability, model/serving pipelines, observability, and engineering standards for application teams. Day-to-day work typically balances architecture decisions, hands-on infrastructure and automation, incident or performance follow-through, and mentoring engineers on safe, scalable AI delivery—grounded in the lead scope implied by the title rather than unpublished internal rituals.
Strong fit usually combines software/platform engineering with production AI operations: containerized services, CI/CD, cloud infrastructure, LLM serving and evaluation loops, monitoring, and security/cost controls. Leadership signals include design reviews, cross-team enablement, and setting LLMOps practices. No certified training paths were supplied in the source certs data.
Lead-level AI application engineering with infrastructure and LLMOps ownership at TechBiz Global.
Listed across Canada, Germany, Ireland, the Netherlands, Sweden, the UK, and the US; site-level detail is not in the source data.
No verified compensation or employee-review figures were provided in the source data.
Website: techbizglobal.com
Public cache only — not an employee review.
TechBiz Global is hiring a Lead AI Application Engineer (Infrastructure & LLMOps) on behalf of one of its top portfolio clients — an on-site, full-time role open to candidates across Canada, Germany, Ireland, the Netherlands, Sweden, the United Kingdom, and the United States. If you thrive on building the foundations that power modern AI at scale, this position offers a high-impact path forward. About the Role As Lead AI Application Engineer, you will own the shared, multi-tenant AI platform that underpins the entire machine learning lifecycle across both cloud and on-premises environments. Your mission is to deliver reliable, fast, and cost-conscious AI infrastructure while empowering product squads to ship AI features faster. Key Responsibilities Design and operate a multi-tenant AI platform spanning hybrid cloud and on-prem, prioritizing high availability, low latency, and efficient spend across all shared resources. Apply LLMOps and MLOps practices, including automated model deployment pipelines. Build a catalogue of reusable AI capabilities — Inference, Embeddings, and RAG offered "as-a-service" — with unified APIs and abstraction layers that keep teams free from vendor lock-in. Deploy and scale vector databases (Pinecone, Milvus, Weaviate) and feature stores (Feast, Tecton, Hopsworks), tuning retrieval for real-time and agentic workloads. Manage model hosting on Kubernetes with GPU orchestration to use compute resources efficiently. Create a self-service portal or CLI, plus templates and blueprints, so squads can provision models, environments, and data stores independently and reduce time-to-inference. Lead internal enablement through documentation and hands-on workshops. Qualifications 8+ years in platform engineering, DevOps, or site reliability engineering (SRE), with 2+ years dedicated to AI/ML infrastructure or platforms. Deep hands-on skill with Kubernetes, Docker, and Terraform or Pulumi. Proven experience running workloads across AWS, Azure, and GCP as well as on-premises (NVIDIA AI Enterprise, OpenShift). Experience serving models with vLLM, TGI, or NVIDIA Triton. Strong command of vector databases alongside SQL and NoSQL systems. High proficiency in Python plus Go or Rust for platform tooling. Prior work building Internal Developer Platforms (IDP) is a significant advantage. About TechBiz Global TechBiz Global is a recruitment partner that connects skilled engineers with leading organizations across its client portfolio,…
Generated for personal interview prep · 2026-07-16 UTC · getajob.ai