Note: The job is a remote job and is open to candidates in USA. Inviso is recruiting for an unnamed client for a remote, permanent Applied AI Engineer position. The role builds and operates production AI capabilities across an enterprise platform, with a focus on reliability, evaluation, safety, and cost-aware delivery.
Responsibilities
- 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
- 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
- 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
Skills
- 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
- 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
- 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
Benefits
- Remote work
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