AI Engineer at ampliphi in Canada

About Us

At Pinpoint Global, we build training and compliance platforms that organizations rely on to keep their teams skilled, certified, and audit-ready. We're part of the Valsoft Edelweiss Software Group — a large-scale portfolio of vertical market B2B SaaS companies — and we operate with a startup mindset: high ownership, high velocity, and real ROI.

Our core stack is ASP.NET MVC + MS SQL — battle-tested and ready to evolve. We're looking for true builders: engineers who orchestrate systems, ship production-grade AI, and modernize massive legacy codebases at speed.

The Role

You'll embed directly in our engineering team, working hands-on inside our .NET codebase and broader product suite. Your mission is to act as an organizational force multiplier — turning legacy inefficiencies into intelligent systems, increasing Net Revenue Retention (NRR), and driving measurable business impact through production-grade AI.

This is a full-stack role with direct product impact. If you enjoy owning problems end-to-end, have a relentless bias toward action, and thrive on shipping real systems at high velocity — this role is for you.

What You'll Do

Build & Ship AI Systems

• Integrate AI capabilities (LLM APIs, intelligent automation, personalization) into our ASP.NET MVC products

• Design, develop, and deploy production-grade, secure AI systems using scalable polyglot microservices

• Integrate enterprise LLMs (Anthropic Claude, OpenAI, Google Gemini) into SaaS platforms via fault-tolerant API routing gateways

• Develop autonomous multi-agent workflows using parallel orchestration tools (Claude Code CLI, OpenAI Codex)

• Build new product surfaces — smart content recommendations, automated compliance tracking, AI-assisted reporting

• Rapidly prototype → validate via adversarial testing → deploy → iterate

Architect AI Infrastructure

• Implement complex RAG pipelines and optimize trade-offs between massive-context hydration and multi-stage semantic retrieval

• Build vector databases (Pinecone, Weaviate, FAISS) and manage persistent document embedding queues

• Build polyglot microservices handling long-running tasks and streaming via WebSockets and Server-Sent Events

• Ensure SOC 2 compliance, data residency controls, and deterministic execution through policy-as-code agentic governance

• Deploy and scale models within secure managed cloud boundaries

Modernize & Collaborate

• Identify high-impact modernization opportunities across our training and compliance platform

• Help migrate legacy features into scalable, AI-native architectures using agentic swarm coding and automated refactoring

• Architect zero-touch CI/CD pipelines with adversarial gating and AI-driven test automation

• Integrate synthetic red teaming into CI/CD to prevent prompt drift, reward hacking, and logic degradation

• Work directly with non-technical stakeholders to translate ambiguous business problems into secure, scalable AI solutions

• Collaborate with product and design to ship features end-to-end

Required Technical Skills

Core Engineering

• 3–5+ years of enterprise software development experience with strong full-stack web fundamentals

• Hands-on experience with .NET / ASP.NET MVC (our core stack) and C#

• Fluency across popular languages and frameworks: Python, JavaScript, TypeScript, .NET, NextJS

• Solid understanding of relational databases — MS SQL experience a plus

• Backend experience designing polyglot APIs, decoupled async microservices, and persistent connection protocols (WebSockets/SSE)

• Familiarity with containerization (Docker, Kubernetes) and multi-region cloud infrastructure (AWS, Azure, or GCP)

AI & ML Systems

• Practical experience integrating AI/ML APIs or building AI-powered features in production

• Enterprise LLM integration and dynamic API routing (OpenAI, Anthropic, Gemini)

• Multi-agent orchestration and advanced CLI tooling (Claude Code, OpenAI Codex)

• Mastery of AI IDE tooling: Cursor for repo-wide reasoning, GitHub Copilot

• RAG pipeline design: dynamic chunking, vector databases, embedding management

• Custom evaluations (LangSmith), structured outputs, and managed fine-tuning in secure cloud environments

MLOps & Process Automation

• Zero-touch CI/CD pipelines and advanced Git workflows (including worktree isolation for autonomous sub-agents)

• Unit test and benchmark automation integrated with AI-driven testing frameworks

• Adversarial LLM testing and automated synthetic red-teaming

• Hallucination mitigation, enterprise guardrails, and deterministic policy-as-code execution

• Real-time token/credit consumption tracking and dynamic access limit monitoring

Bonus Points

• Experience with prompt engineering, RAG pipelines, or agentic workflows

• Familiarity with refactoring or re-architecting legacy .NET applications

• Background in regulated industries: HR tech, e-learning, compliance, LMS platforms

• Knowledge of SOC 2 audit requirements and compliance automation tooling

Who You Are

• Highly hands-on — you ship, not just design; you operate at velocity by orchestrating autonomous tools

• Entrepreneurial startup mindset — you spot legacy inefficiencies and act with a bias toward rapid action

• Comfortable with ambiguity — you scope, own, and deliver independently

• Business-oriented — you care about ROI, operational leverage, and financial metrics like NRR and CAC

• Strong product instincts — you think about the user, not just the implementation

• Fast learner — you track real-time shifts in AI ecosystems, APIs, and infrastructure

• Pragmatic — you use the right secure enterprise tool for the bottleneck, not just the flashiest one

Tech Stack

ASP.NET MVC · C# · MS SQL · Python · TypeScript · LLM APIs (Anthropic, OpenAI, Gemini) · RAG / Vector DBs · Docker · Kubernetes · Azure / AWS / GCP · REST APIs · WebSockets · HTML/CSS/JS

Why Join

• Shape the AI strategy of a growing compliance and training platform

• Own meaningful features from day one — not just tickets in a backlog

• Real modernization challenge: legacy codebase + greenfield AI opportunity in parallel

• Collaborative, low-ego team that ships and iterates fast

• Startup velocity inside an established, well-resourced corporate portfolio