Engineering — Artificial Intelligence
AI/ML Engineer
Design and ship production AI systems — LLM applications, retrieval architectures, and ML pipelines — for enterprise clients. You will own solutions end to end, from data readiness and model selection through evaluation, deployment, and monitoring.
About the role
Most AI work stalls between a promising demo and something a business can actually depend on. This role lives in that gap. You will take problems that arrive as vague ambitions — "we want to use our documents better," "can this be automated" — and turn them into systems with measurable behaviour, known failure modes, and a cost per request somebody can sign off on.
You will work close to the client, often as the only AI specialist in the room, which makes the job part engineering and part translation. Expect to build retrieval pipelines and evaluation harnesses one week and explain to a sceptical executive why a confident-sounding wrong answer is the real risk the next. We care more about judgment on what to ship than familiarity with any particular framework.
What you'll do
- Build and productionize LLM-based applications, RAG systems, and ML models
- Design evaluation harnesses and quality baselines for AI features
- Implement MLOps practices: versioning, monitoring, and lifecycle management
- Collaborate with clients to translate business problems into AI solutions
- Contribute to SparkSolutions' internal AI platforms and accelerators
What you'll bring
- 3+ years of software engineering experience, with 1+ years shipping ML/AI in production
- Strong Python and experience with modern AI tooling (LLM APIs, vector stores, orchestration frameworks)
- Experience with cloud platforms (AWS, Azure, or GCP) and containerized deployment
- Clear communication with technical and business stakeholders
Nice to have
Genuinely optional. Apply if the sections above describe you — we have never expected one person to bring all of these.
- Experience designing evaluation sets and measuring model quality beyond spot-checking
- Background in information retrieval, search relevance, or recommendation systems
- Exposure to data privacy and governance requirements in regulated industries
- Open-source contributions, published work, or a portfolio of shipped AI projects
Your first 90 days
What success looks like early on, so you know how you will be measured before you accept.
- 01Ship a scoped AI feature to production for a live client engagement
- 02Establish an evaluation baseline for an existing AI system so quality changes become visible
- 03Document a reusable pattern — retrieval, prompting, or deployment — the wider team can build on
Tools you'll work with
- Python
- LLM APIs
- Vector databases
- Docker
- AWS / Azure / GCP
- CI/CD
Apply for this role
Four short steps: about you, your experience, your resume, then a review before anything is sent. Takes about five minutes.
Apply NowWe review every application and respond to every candidate. Your resume is stored privately and is never published or shared. SparkSolutions is an equal opportunity employer; accommodations are available on request throughout the hiring process.
Prefer email? Write to ignite@sparksol.ca.