Principal AI Engineer building AWS - based GenAI and AI/ML solutions for Sun Life’s insurance and financial - services contact centers. Driving architecture, DevOps, deployment, and engineering standards.
About the role
- Applied AI Engineer building reliable, evaluated, and monitored production AI systems for Inviso’s enterprise clients. Designing agent runtimes, RAG, guardrails, and model-routing capabilities.
Responsibilities
- Support an external software development organization as part of Inviso’s delivery team
- Build and operate AI capabilities across an enterprise platform
- Implement agent runtimes, model routing, RAG, tool/function calling, guardrails, evaluation, monitoring, cost management, latency optimization, and production quality controls
- Move AI capabilities from prototypes to monitored production systems
- Evaluate AI behavior rigorously
- Partner with product and engineering teams to determine when AI is appropriate
- Turn experiments into useful, reliable, measurable, safe, and cost-aware production features
- Build evaluation discipline into the delivery process
- Help client teams make informed decisions about where AI creates value
Requirements
- 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
- 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 focused on business value and responsible delivery
- 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 with strong review, testing, and safety discipline
Benefits
- Annual $2,000 training allowance
- Paid time off
- Paid holidays
- Other benefits
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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