General Information:

Job Title: Senior Engineer, Applied AI
Location: Toronto, ON (Onsite/Hybrid)
Job Type: Full-Time
Hiring Timeline: Immediate
Reporting Line: Head of R&D
Existing Vacancy: Yes
Salary Range: 135K - 170K CAD per year (negotiable)

Role Overview:

This is a high-impact, senior engineering role, where engineers are expected to operate with significant ownership and minimal oversight. The role focuses on building production-ready AI systems in an environment where speed, correctness, and architectural decisions have long-term implications.

Ideal Candidate's Profile:

A seasoned AI engineer (ninja-level) with hands-on experience in developing and deploying real LLM systems, who excels in environments with significant ownership responsibilities and values impactful work more than structured, low-risk settings.

Individuals driven by ownership, autonomy, and the opportunity to build from the ground up (rather than being a small cog in a large organization) will thrive here.

Responsibilities & Expectations:

Key Responsibilities:

• Design and build production-grade LLM systems (RAG, agents, APIs)
• Architect systems that minimize rework in fast-evolving environments
• Own end-to-end delivery of critical AI features
• Define and implement evaluation frameworks
• Optimize systems for cost, latency, and reliability
• Collaborate across teams where needed
• Provide technical guidance where applicable (especially for less experienced engineers on adjacent teams)

Must-Haves (non-negotiables):

• Strong backend/software engineering foundation (Python, APIs, system design)
• Proven experience shipping LLM-powered features to production (non-negotiable)
• Deep expertise in:

• RAG systems (advanced retrieval + evaluation)
• LLM evaluation methodologies (golden sets, regression testing)
• Prompt engineering at API level
• Agent architectures (ReAct, tool calling, planning loops)
• Strong understanding of trade-offs (cost, latency, scalability)
• Ability to work independently in ambiguous, fast-moving environments

Nice-to-Have:

• Fine-tuning experience (LoRA, SFT, DPO)
• Inference stack experience (vLLM, TGI, llama.cpp)
• Observability tooling (Langfuse, LangSmith)
• Prior experience in early-stage or high-ownership teams

• Public work (GitHub, blogs, talks) demonstrating depth

Education:

• Bachelor's or master's degree in computer science or a related discipline

Technical Skills:

• Advanced Python and backend engineering
• LLM systems (RAG, agents, prompting, evaluation)
• API design and system architecture
• Docker, Git, CI/CD
• Understanding of inference systems and scaling

Soft Skills:

• High ownership and accountability
• Ability to operate in ambiguity ("build while flying")
• Strong decision-making and trade-off analysis
• Clear communication with cross-functional teams

What Success Looks Like in the First 90 Days:

By the end of Month 1:

• Deeply understand Crosstalk/Zync architecture and ongoing projects
• Contribute meaningfully to ongoing systems (not just onboarding tasks)
• Identify gaps or risks in current implementations

By the end of Month 2:

• Own and deliver a critical feature or system component end-to-end
• Improve an existing system (performance, evals, or architecture)
• Demonstrate strong independent execution

By the end of Month 3:

• Act as a trusted senior engineer on the team
• Drive architectural decisions or improvements
• Deliver measurable impact (system reliability, quality, or efficiency)
• Operate with minimal oversight in high-stakes projects

Perks you'll appreciate:

• Employee Health: Comprehensive health and dental coverage for you and your family
• Time Off: Competitive paid time off and flexible leave policies
• Retirement: Retirement savings programs and employer contributions
• Professional Growth: Dedicated learning and development budget
• Flexible Work: Remote and hybrid work options
• Perks: Equipment allowances, internet reimbursements, business travel coverage, and employee stock options (ESOP), where applicable.
• Community Engagement: Team events, meetups, and company offsites

Use of Artificial Intelligence in Hiring:

FIQ uses artificial intelligence to assist in the screening, assessment, and shortlisting of applications for this position, including features within our applicant tracking system. AI supports human reviewers and does not make hiring decisions on its own. Final hiring decisions are made by FIQ personnel. If you have questions about how AI is used in this process, contact [redacted].

Accommodations:

FIQ is committed to an inclusive and accessible recruitment process. Consistent with the Accessibility for Ontarians with Disabilities Act (AODA) and the Ontario Human Rights Code, accommodations are available on request at any stage of recruitment and assessment. Contact [redacted] and we will work with you to meet your needs.

Candidate Privacy:

Your personal information is collected and used for recruitment purposes in accordance with FIQ's Candidate Privacy Notice, available here, and in accordance with applicable Canadian privacy law (PIPEDA). This includes the use of AI-assisted screening described above and cross-border storage or processing that may occur in the United States. By applying, you acknowledge this notice. Questions: [redacted].