About Torinit

Torinit is a technology services company headquartered in Toronto. We build enterprise AI and data systems for mid-market and enterprise clients, mostly work that turns data a company already holds into something its customers benefit from. A lot of enterprise AI stops at the pilot. Ours goes into client production environments, through their security review, handling their customers' data. That is a harder bar than a demo and it is the one we build to. The Role

This is the role that decides how Torinit builds AI. You will lead engineering as a player-coach, reporting to the Partner, with a hand in both the work we deliver and the team delivering it.

Most of our pipeline is applied AI: retrieval and extraction systems, agentic workflows, model-driven features headed for enterprise production. Roughly 30% of your time is client-facing technical work. That means solution architecture on our most complex engagements, design reviews, and the problems nobody else can unblock.

The other 70% is yours to invest in the practice, in the standards and the engineers and the hiring bar that decide what we can take on next. You will be part of our Canadian team of five, leading an engineering group of 20+ across our India office, a mix of full-time engineers and contractors with senior engineers and leads already in place. This is one engineering organization rather than a head office and an outsourced arm, and the standards you set apply across all of it.

You will work day to day with those leads, alongside Consultants, Project Managers and Designers. Expect some early-morning overlap with the India team as part of the rhythm.

Responsibilities

Own our technical approach to applied AI. How we architect retrieval, extraction and agentic systems, how we measure whether the output is good enough, and what has to be true before a model-driven feature reaches a client's production environment.

Own solution architecture across engagements, from technical discovery and system design through estimation and the decisions that carry a project from proposal to production.

Set the quality bar for how we build. Security, performance and observability expectations that hold across every engagement, including the ones specific to AI systems: evaluation, data handling, cost, and what the thing does when it fails. You define what done means on new projects. Exceptions get agreed with the Partner rather than assumed.

Standardize our delivery platform. CI/CD pipelines, container practices, environment management and infrastructure-as-code, applied the same way across projects.

Own technical hiring. You run the interview loop and have final sign-off on engineering hires. We want engineers who can sit with a client, understand the business problem and own the technical conversation.



You define that profile and hire against it.

Set how we use AI in our own engineering, covering coding tools, agentic workflows, guardrails and client-safe usage policies. We expect this to change how the team works and you decide how.

Build the engineering habits we want. Architecture reviews, decision records, research before building, and mentoring that grows engineers into senior client-facing roles.

Support sales on larger or technically complex opportunities, through scoping, estimation and technical credibility in the room.

Required Skills & Experience

10+ years in software engineering, including production ownership at enterprise scale. Performance, security and observability are second nature to you.

Applied AI you have actually shipped rather than piloted. Retrieval, extraction pipelines or agentic systems, with a real method for judging output quality before it goes live. You can walk us through what you built, what it got wrong, and how you knew.

You have introduced engineering standards across multiple teams under client deadlines and made them stick. Services or consultancy experience is strongly preferred.

Credibility with enterprise stakeholders. You can scope work, present and defend technical decisions, and push back when it matters.

Led architecture for enterprise systems end to end, from discovery through delivery, with buy-vs-build judgment you can walk us through.

Track record growing senior engineers and running technical hiring.

Experience leading a distributed team across time zones, including working through senior engineers and leads rather than only managing directly.

Preferred

Depth in TypeScript/Node and React, with working Python. That is our primary stack.

AWS, Docker/Kubernetes, GitHub Actions or equivalent, and infrastructure-as-code. Ideally you have built delivery standards and not only worked within them.

Multi-cloud delivery, with Azure or GCP alongside AWS

Modern data platform work: ETL/ELT pipeline design, warehouse and lakehouse architectures such as Databricks or Snowflake, and the modelling decisions that keep analytics and AI workloads reliable at scale

Active use of AI coding tools and agentic workflows in your own work

Cost and performance engineering for AI workloads, including inference spend, caching, and the tradeoffs that decide whether a feature is viable at a client's volume

Practice lead or delivery architect experience at a consultancy





What Success Looks Like in Year One

Our standards for security, performance and observability are written down, adopted across engagements, and holding without you policing them.

We have a real method for evaluating AI output quality, applied consistently before anything model-driven reaches a client's production environment.

The delivery platform is standardized enough that a new engagement starts from a known baseline instead of a blank page.

You have defined the engineering profile we hire against and made hires with it, and the leads in India are operating with more autonomy than they have today.

Why Torinit

Most engineering leaders get one shot at applied AI, inside one product, on one dataset. Here you get many. Different industries, different data, different constraints, different ideas of what production means. These are also AI systems that have to survive contact with a real business: an enterprise security review, real customer data, and accuracy that holds up for someone whose job depends on it. That is the harder and more interesting version of this work.

What We

Offer

Health Insurance: 100% employer-paid

Time Off: 3 weeks vacation, statutory holidays, and 5 paid sick days

Professional Development: Annual budget for certifications, courses and conferences, including speaking and community involvement where it serves the practice

Work Arrangement: Hybrid, 3 days a week in the Toronto office and 2 days remote

Scope: First dedicated engineering leader at Torinit, across a 25+ person engineering organization. You will be setting the first standards here rather than inheriting someone else's, and they hold across every engagement we take from here.

Growth Path: Broaden into full ownership of the engineering practice as we scale, with capability lines and delivery leadership under you The Work: Applied AI at the core of the portfolio. Retrieval, extraction and agentic systems going into enterprise production, plus the data platform work underneath them, across several industries and security regimes.

Interview Process

Four stages, typically over three to four weeks:

Intro screen, 15 to 20 minutes.

Leadership and judgment conversation, 45 minutes.

Technical deep dive, 60 to 90 minutes, on systems you have actually built.

Working session, 90 minutes, on a real sanitized problem from our pipeline, with a few days to prepare.

Accommodation and Equal Prospect

Torinit is an equal opportunity employer. We welcome applications from people of all backgrounds and we hire on merit and fit for the role. We provide accommodation for applicants with disabilities at every stage of our recruitment process. If you need accommodation at any point, let us know and we will arrange it with you.

📌 Head of Engineering (Toronto)
🏢 Torinit
📍 Toronto