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Added 2 mins ago
Sr Associate Platform Engineer
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Contributes to the overall success of the Senior Associate Cloud Platform Engineering function by ensuring long‑term technical vision and roadmap is defined and maintained, focusing on…
Added 17 mins ago
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Added 32 mins ago
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systems integration engineer - software
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Conduct tests and perform security and quality controls

Network Solutions

Senior Applied AI Engineer

Canada

Full-time

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Job details

Job type: Full-time
Posting language: English
Posted: Oct 6, 2026

Original job description

At Network Solutions, we’ve been trusted for decades to help people get online and stay ahead. We’ve been here since the beginning of the internet, and we’re still building for what comes next.

As the original digital identity authority, we help secure domain names, protect brands, and safeguard the infrastructure businesses rely on. We empower our customers to own and manage the assets that define them online, while delivering enterprise-grade security to protect against virtual threats. Our team leverages modern, AI-accelerated tools to streamline how businesses manage their digital presence, making the most of our decades of experience.

The Network Solutions team is here to help online businesses protect what’s theirs and build for tomorrow. That’s why millions trust us to protect their domains, brands, and websites every day.

Senior Applied AI Engineer

The impact you’ll make

We are looking for a hands on Senior Applied AI Engineer who combines strong software engineering with practical AI problem solving. You will build production AI experiences across Network Solutions, including conversational agents, business and website creation, knowledge and FAQ agents, content experiences, domain discovery, and intelligent automation.

You will take ambiguous customer and product problems, evaluate different approaches, and independently turn the strongest solution into reliable, secure, observable, and cost efficient production systems. This is an applied engineering role focused on shipping real AI products.

What you’ll do

Translate loosely defined product problems into practical AI solutions, choosing between LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software.
Design and build production AI services using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools.
Build reliable tool calling agents and multi step workflows that interact with internal APIs, MCP tools, business systems, and knowledge sources.
Design event driven systems using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including retries, dead letter handling, idempotency, back pressure, and failure recovery.
Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations.
Experiment with models from OpenAI, Anthropic, Google, xAI, and open weight ecosystems, selecting the right model for quality, latency, reliability, and cost.
Create AI evaluation pipelines using curated datasets, regression tests, retrieval metrics, LLM as judge techniques, groundedness checks, and tool execution evaluation.
Diagnose hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs.
Operate as an AI powered engineer using Cursor, Claude Code, OpenAI Codex, or equivalent coding agents to accelerate design, implementation, testing, debugging, and refactoring.

What we’re looking for

5 or more years of professional software engineering experience building production backend, distributed, or cloud based systems.
Hands on experience building Applied AI, LLM, RAG, NLP, or agent based applications, with meaningful production exposure.
Advanced Python skills including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API development.
Strong backend and distributed systems fundamentals including REST APIs, concurrency, background processing, caching, reliability, and production debugging.
Production experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging architectures.
Hands on experience integrating LLM APIs and building structured output, function calling, tool calling, or agent execution workflows.
Experience building at least one RAG or knowledge grounded system with measurable quality and latency outcomes.
Strong PostgreSQL and data modeling skills, with experience using Redis, pgvector, vector databases, or hybrid search technologies.
Demonstrated use of AI coding agents such as Cursor, Claude Code, Codex, or equivalent tools as a core part of daily software engineering.

Applied AI engineering knowledge

You should be comfortable going beyond a working demo and understand how modern AI systems behave, fail, and scale in production.

Strong understanding of prompting, structured outputs, tool schemas, context management, model routing, retries, fallbacks, rate limits, and streaming responses.
Ability to design experiments and evaluations that compare approaches using measurable outcomes rather than subjective testing alone.
Understanding of retrieval quality, grounding, hallucination mitigation, prompt injection risks, tool authorization, and practical agent guardrails.
Experience with Docker, CI/CD, automated testing, observability, distributed tracing, OpenTelemetry, Langfuse, or similar tooling.

How you’ll work

You will often start with an ambiguous customer or product goal rather than a detailed implementation plan. You should be able to independently break the problem down, validate assumptions, choose an approach, and drive the solution through production.

Turn product goals into testable technical hypotheses and rapidly prototype alternatives when needed.
Choose the simplest architecture that solves the problem well rather than using an agent or LLM by default.
Define quality, latency, reliability, safety, and cost targets and measure production performance against them.
Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support.
Use AI coding agents aggressively to increase engineering velocity while preserving architecture quality, testing discipline, security, and maintainability.

What will make you stand out

Experience with Semantic Kernel, Microsoft Agent Framework, LangGraph, PydanticAI, or equivalent agent frameworks.
Experience building or consuming Model Context Protocol tools and servers, or working with model gateways such as LiteLLM.
Experience with Azure AI Search, pgvector, Chroma, Kubernetes, Azure, OCI, or similar production AI platform technologies.
Experience building high scale customer facing AI products, open weight model inference, AI safety controls, or automated agent evaluation systems.

Strong problem solving, excellent engineering judgment, and the ability to independently move from ambiguity to a production quality solution are the foundation for this role.

#NetworkSolutions

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More about this job

Responsibilities

Build and operate production AI products and services, including conversational agents, RAG systems, tool-calling workflows, and intelligent automation. Independently turn ambiguous product needs into secure, reliable, observable, and cost-efficient solutions, and evaluate their quality and performance in production.

Requirements

Requires at least five years of professional software engineering experience, hands-on production experience with applied AI or LLM applications, and advanced Python backend development skills. Candidates should also have experience with distributed systems and messaging, LLM integrations and RAG, PostgreSQL and related data technologies, AI coding agents, and production testing and observability.

Skills

Python
FastAPI
Asynchronous Programming
Distributed Systems
PostgreSQL
Redis
RabbitMQ
Kafka
Azure Service Bus
Large Language Models
Retrieval-Augmented Generation
Agent Workflows
AI Evaluation
Docker
CI/CD
Observability

Visa sponsorship

Not detected in the job text

Keywords

Python
FastAPI
Pydantic
SQLAlchemy
SQLModel
PostgreSQL
Redis
RabbitMQ
Kafka
Azure Service Bus
OpenAI
Anthropic
Google
xAI
Large Language Models
Retrieval-Augmented Generation
Vector Databases
pgvector
Hybrid Search
Tool Calling
Model Context Protocol
Docker
CI/CD
OpenTelemetry
Langfuse
Cursor
Claude Code
OpenAI Codex
Kubernetes
Azure AI Search

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