Forward Deployed AI Engineer
$115,000–$130,000 year
HybridToronto, Ontario, Canada or Montréal, Quebec, Canada
Job Summary
Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on delivery. Locate, qualify, and secure access to required data while translating use cases into production-ready GenAI and agentic AI solutions. Prototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain experts. Balance speed, quality, cost, security, and maintainability while making clear technical and delivery trade-offs. Define success criteria from the outset, including adoption, performance, reliability, risk, cost, and measurable business value. Ensure solutions are documented, governed, and transferable so clients can operate them with confidence. Turn successful delivery into reusable patterns, accelerators, and building blocks that strengthen future engagements.
Required Qualifications
- Typically 5-10 years of relevant experience in AI Engineering, Machine Learning, Software Engineering, Data Engineering, or technical consulting
- Hands-on experience delivering AI, GenAI, or software solutions into production
- Experience working directly with clients or in complex stakeholder environments
- Evidence of turning complex use cases into adopted measurable solutions
- A degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field - or equivalent practical experience
- Strong Python development skills
- API integration experience
- solid software-engineering practices
- Hands-on experience with LLMs and GenAI
- experience with RAG
- experience with embeddings
- experience with vector search
- experience with AI agents
- experience with agentic workflows
- Familiarity with AI frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, AutoGen, or similar
- Experience integrating and deploying AI solutions within enterprise environments
- Experience deploying AI solutions on cloud platforms (Azure, AWS, or GCP)
- Working knowledge of Docker
- Working knowledge of Git
- Working knowledge of CI/CD
- Working knowledge of MLOps/LLMOps
- Working knowledge of monitoring
- Working knowledge of security
- Working knowledge of data privacy
- Working knowledge of governance
- Working knowledge of responsible AI principles
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