Forward Deployed Engineer

Toronto, ON
Hybrid
Posted Oct 15, 2026
1 position

$140,000–$180,000 / year

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Employment type: Full-time
Experience level: Lead · 10+ years
Minimum education: Bachelor’s degree
Posting language: English
Working hours: 40 hours per week
Seniority: Mid-Senior level
Application method: Direct apply is available

Job summary

Embed with client teams to understand workflows and data, then design, validate, and deploy scalable AI applications and data pipelines from prototype through production. Implement MLOps/LLMOps, analyze errors, resolve technical blockers, monitor and stabilize launched solutions, and help clients measure impact and adopt the solutions.

Job details

Join us as a Forward Deployed Engineer and be at the forefront of transforming complex data challenges into innovative AI-powered solutions that make a real impact. This role offers a unique opportunity to embed directly with clients, shaping the future of enterprise data and AI applications through hands-on engineering, collaboration, and strategic problem-solving. In this role, you’ll work closely with business users, product owners, and engineering teams to understand workflows, analyze data, and drive solutions from prototype to production. You’ll be instrumental in designing and deploying scalable data pipelines, developing AI applications using cutting-edge tools like Claude, prompt engineering, and vector databases, and implementing MLOps/LLMOps capabilities to ensure reliable, secure production environments. You’ll also support adoption and measure success, providing ongoing stabilization, incident resolution, and optimization to maximize business value. Start Date: As soon as possible Location: Toronto, ON Hybrid Salary: 140K - 180K Key Responsibilities Include Embedding with client teams to map workflows, data, and decision points Validating and hardening solutions using real client data and APIs Designing and deploying enterprise AI and data applications across cloud platforms Building and managing batch, real-time, and streaming data pipelines Implementing MLOps/LLMOps best practices for lifecycle and deployment automation Conducting structured error analysis and prioritizing improvements Resolving technical blockers related to data access, security, and infrastructure Monitoring post-launch performance, ensuring reliability, and supporting stabilization Collaborating with clients to track KPIs, measure impact, and facilitate knowledge transfer Must-Have Qualifications 10+ years of experience in data engineering, AI application development, and cloud data platforms Expertise in Python, PySpark, SQL, REST APIs, FastAPI, Git, Docker, Kubernetes, Airflow, MLflow, and CI/CD Experience with Claude, prompt engineering, RAG, vector databases, and agentic AI patterns Proven ability to operationalize MLOps/LLMOps, including deployment, monitoring, and support in production Strong client-facing skills with experience in discovery, prototyping, and technical presentations Bachelor’s degree in a relevant discipline; certifications in cloud, Kubernetes, AI engineering, or security are a plus Nice-to-Have BFSI industry experience Additional certifications in cloud, security, or architecture Ready to shape the future of enterprise AI and data solutions? Apply now and be part of a team driving innovation and impact.

What you’ll do

Requirements

Requires 10+ years of experience in data engineering, AI application development, and cloud data platforms, with expertise in the listed engineering, cloud, and AI technologies. Candidates must have production MLOps/LLMOps experience, strong client-facing discovery and presentation skills, and a bachelor's degree in a relevant discipline.