Applied AI Engineer

Jobgether; Canada; On-site; CAD 120,000 - 180,000; Full time

Benefits offered by this job

Founding AI engineering team

Ownership of production AI systems

Collaboration across teams

Influence AI strategy and safety

Exposure to enterprise-scale AI models

Job summary

Jobgether is seeking an Applied AI Engineer based in Canada to build and operate the core AI infrastructure powering reliable, secure, and scalable AI features in production.

You will design model routing, implement RAG and grounding, develop evaluation frameworks, and ensure safety controls while collaborating with product teams and customers.

This senior role offers ownership of production AI systems, influence over architecture and safety standards, and opportunities to mentor future engineers.

Qualifications

• Demonstrated experience shipping software powered by large language models or ML to production users.
• Strong understanding of AI evaluation methods and systematic assessment of model and agent quality.
• Ability to treat cost and latency as engineering constraints in AI-powered systems.
• Clear communication skills to explain technical decisions to engineers, product stakeholders, and customers.
• Pragmatic, outcomes-focused mindset prioritizing customer value and practical delivery.

Responsibilities

• Build and operate the AI runtime, tool-calling capabilities, and orchestration infrastructure for production agent workflows.
• Design model routing strategies balancing performance, latency, and costs.
• Improve RAG, grounding, and context-retrieval systems to boost output quality.
• Develop evaluation harnesses, datasets, and benchmarks for model and agent performance.
• Establish evaluation frameworks with guardrails and safety controls for untrusted inputs.
• Own production AI behavior including reliability, latency, cost, and failure management.
• Collaborate with product teams to determine when agent-based solutions are appropriate.
• Partner with customers to validate outcomes and identify high-value use cases.
• Prove value of focused solutions before scaling successful implementations.
• Instrument AI systems and measure impact to guide decisions.
• Integrate automated QA, testing, gates, and regression checks into pipelines.
• Contribute to engineering standards and future team growth through interviews and mentoring.
• Promote technical excellence and customer-focused collaboration across the organization.

Skills

LLM experience

Evaluation methods

Cost & latency awareness

Communication

Tools

Python

C#/.NET

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an Applied AI Engineer based in Canada.

As an Applied AI Engineer, you will build and operate the core AI infrastructure that powers reliable, secure, and scalable AI features in production environments. You will focus on AI runtime architecture, agent orchestration, retrieval-augmented generation (RAG), evaluation frameworks, and safety controls. Working with proven commercial models, you will turn advanced AI capabilities into practical solutions that support mission-critical customer workflows. You will join a small, senior founding engineering team and help establish its technical standards, development practices, and evaluation discipline. The role combines hands-on engineering with close collaboration across product teams and customers. You will have the opportunity to shape a growing AI platform while ensuring that performance, cost, reliability, and measurable business value remain central to every delivery.

Accountabilities:

• Build and operate the AI runtime, tool-calling capabilities, and orchestration infrastructure required for production agent workflows.

• Design model selection and routing strategies, balancing performance, latency, and operating costs.

• Implement and improve retrieval-augmented generation (RAG), grounding, and context-retrieval systems where they demonstrably improve output quality.

• Develop evaluation harnesses, golden datasets, and quality benchmarks to assess model and agent performance, support release decisions, and identify regressions or performance drift.

• Establish evaluation frameworks for AI quality and implement guardrails and safety controls, treating retrieved information and tool outputs as untrusted inputs.

• Take ownership of production AI behaviour, including reliability, latency, cost, and failure management.

• Collaborate with product teams to determine when agent-based solutions are appropriate and when alternative approaches will deliver better results.

• Partner with customers and product stakeholders to validate outcomes, identify valuable use cases, and prioritize development based on measurable impact.

• Apply an iterative, value-driven approach by proving the value of focused solutions before scaling successful implementations.

• Instrument AI systems and measure their impact, using evidence and performance data to guide product and engineering decisions.

• Integrate automated quality assurance, testing, quality gates, and regression checks into development and delivery pipelines.

• Help establish engineering standards and evaluation and safety practices across the team, while contributing to technical interviews, mentoring, and future team growth.

• Promote a culture of technical excellence, customer focus, pragmatic decision-making, and effective collaboration with product teams across the organization.

Requirements:

• Demonstrated experience shipping software powered by large language models (LLMs) or machine learning (ML) to real production users, beyond prototypes, demonstrations, or experimental notebooks.

• Strong understanding of AI evaluation methods and the ability to assess model and agent quality systematically rather than relying solely on prompt experimentation.

• Ability to treat cost and latency as fundamental engineering constraints when designing and delivering AI-powered systems.

• Clear communication skills, with the ability to explain technical decisions and trade-offs to engineers, product stakeholders, and customers.

• A pragmatic, outcome-oriented mindset, with the ability to prioritize work based on customer needs, measurable value, and practical delivery considerations.

• Strong engineering judgment and the ability to make informed decisions about where AI agents are appropriate and where simpler solutions are more effective.

The following experience is considered an advantage, but is not required:

• Experience building and operating production-grade AI agents or RAG systems, including understanding their operational challenges and failure modes.

• Experience implementing model routing strategies or delivering measurable reductions in AI infrastructure and operating costs.

• Experience building evaluation harnesses that inform release decisions, including LLM-as-a-judge approaches calibrated against human assessments.

• Familiarity with model fine-tuning, distillation, or other model optimization techniques.

• Strong programming skills in Python or C#/.NET.

• Experience grounding AI applications in structured, well-modeled business data.

• Background in system and platform architecture, including the ability to evaluate technical trade-offs and long-term implications.

• Experience developing or operating enterprise-scale software, with an understanding of reliability, security, and integration requirements.

• Productive use of AI-assisted coding tools, with the technical judgment to review, validate, and supervise their output.

Benefits:

• Opportunity to join a founding AI engineering team and help shape the platform's architecture, technical standards, and future development.

• Meaningful ownership of production AI systems supporting mission-critical customer workflows.

• Close collaboration with experienced engineering and product professionals across multiple teams.

• Opportunity to influence AI strategy, evaluation practices, safety standards, and team development.

• Exposure to practical applications of commercial AI models, agent orchestration, retrieval systems, and enterprise-scale software challenges.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.