Build and operate production AI capabilities across enterprise platforms, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, cost controls, and latency optimization. Ship reliable AI features, establish evaluation and quality processes, monitor production behavior, and collaborate with backend, platform, product, security, and client teams to deliver useful, safe, measurable, and cost-aware solutions.
Applied AI Engineer
Inviso is partnering with one of our clients to find an experienced Backend / Platform Engineer to join their team working remotely. Inviso is managing the recruitment and placement process for this opportunity. This is a remote, direct-hire permanent employee position for our client that is a global multi-billion dollar software conglomerate.
The Applied AI Engineer is responsible for building and operating AI capabilities across an enterprise platform, including agent runtimes, model routing, RAG, tool/function calling, guardrails, evaluation, monitoring, cost management, latency optimization, and production quality controls.
This role is focused on shipping AI that works in production, not demos. The ideal candidate understands how to move from prototype to monitored production systems, can evaluate AI behavior rigorously, and can partner with product and engineering teams to decide when AI is the right solution and when it is not.
Experience & Skills Required
- Experience shipping software backed by LLMs, ML models, RAG systems, or agentic workflows to real production users.
- Experience designing or implementing agent runtime, orchestration, model routing, tool/function calling, or AI workflow patterns.
- Strong understanding of RAG, grounding, retrieval quality, prompt design, context management, and evaluation approaches.
- Experience building evaluation harnesses, golden sets, regression checks, quality gates, or measurable AI performance frameworks.
- Ability to treat cost, latency, reliability, and safety as first-class engineering constraints.
- Experience building guardrails and controls for AI systems, including untrusted retrieved content, tool output risk, and failure modes.
- Experience monitoring production AI behavior and improving systems based on evidence, not opinion.
- Strong software engineering skills and ability to collaborate with backend, platform, product, and security teams.
- Strong communication and collaboration skills for client-facing consulting environments.
- Pragmatic mindset with the ability to prioritize business value and ship useful AI capabilities quickly and responsibly.
Desired Qualifications
- Experience with Claude, GPT, Azure OpenAI, open-source models, model routing, fine-tuning, distillation, or small/edge models.
- Experience with MCP, A2A, multi-agent orchestration, AI tool calling, or agent evaluation.
- Experience with LLM-as-judge approaches calibrated against human evaluation.
- Experience with semantic knowledge management, ontologies, knowledge graphs, or semantic layers.
- Experience with AI development lifecycle practices across data, build, evaluation, deployment, monitoring, and continuous improvement.
- Experience using AI-assisted development tools while maintaining strong review, testing, and safety discipline.
Success Criteria
Success in this role is measured by the ability to deliver AI capabilities that are useful, reliable, measurable, safe, and cost aware. Applied AI Engineers are expected to turn experiments into production features, build evaluation discipline into the delivery process, and help client teams make informed decisions about where AI creates real value.
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