- Company: TechBiz Global
- Location: Canada, Germany, Ireland, Netherlands, Sweden, United Kingdom, United States
- Full Time
- Canada, Germany, Ireland, Netherlands, Sweden, United Kingdom, United States
- Apply on company site
Tech Biz Global at a glance
techbizglobal.comTechBiz Global (techbizglobal.com) is hiring a Lead AI Application Engineer focused on infrastructure and LLMOps. Public listing data for this role is limited beyond the company domain and multi-country posting footprint.
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, helping talented professionals find roles where they can grow in innovative, forward-looking environments.
How to Apply
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Terms used in this posting
- hybrid
- A work arrangement combining both in-office and remote/at-home work, typically on a set schedule.
Explore TechBiz Global online
Working in Canada
The posting spans Canada, Germany, Ireland, the Netherlands, Sweden, the United Kingdom, and the United States. Canada—listed as a primary location label—is a North American country of ten provinces and three territories, stretching Atlantic to Pacific and into the Arctic, with the world’s longest coastline and the longest international land border (with the U.S.). Exact office or hybrid site details are not provided in the source data.
🇺🇸 Relocation safety for US: Exercise Normal Caution — via Warnely, CC BY 4.0
National unemployment rate in US: 4.2% — via World Bank
Private-sector wage growth (year over year): 3.3% — via FRED
National quits rate: 1.9% — via FRED (BLS JOLTS)
Weekly initial unemployment claims: 215,000 — via FRED
GDP per capita in US: $90,027 — via World Bank
Consumer price inflation in US: 2.9% (annual) — via World Bank
Real GDP growth in US: 2.2% (annual) — via World Bank
Average hours worked per year in US: 1,800 — via OECD
Build the skills for this role
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.
- Be ready to walk through production LLM or ML platform designs: serving, scaling, evals, and failure modes.
- Discuss infrastructure trade-offs (latency, cost, reliability, observability) for AI applications.
- Prepare examples of leading engineers or standards without inventing employer-specific processes.
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.
Job details above are provided by the employer/source. The sections on this page are compiled from public data sources with AI assistance.
Accommodations: if you need a workplace accommodation to apply for or perform this job, see ADA.gov or EEOC.gov for guidance on your rights and how to request one.
Listing facts
- Role Lead AI Application Engineer (Infrastructure & LLMOps)
- Employer Tech Biz Global
- Location Canada, Germany, Ireland, Netherlands, Sweden, United Kingdom, United States
- Type Full Time
- Posted June 21, 2026
- Apply by 2026-07-21
- Country context US
- Overview Full original description on this page (385 words; rewritten for clarity, not a teaser paste)
Facts above come from this job record on Get A Job.AI — not copied from third-party review sites.
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