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Added 10 mins ago
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HRConnects

Forward Deployed Engineer

Toronto, ON, Canada

$140,000 to $180,000 CAD / year · Full-time

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Applications open in a new tab.

Job details

Pay: $140,000 to $180,000 CAD / year
Job type: Full-time
Apply by: Nov 25, 2026
Experience: 10+ years of experience in data engineering, AI application development, and cloud data platforms
Posting language: English
Posted: Oct 26, 2026

Original job description

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.

Apply About employer

More about this job

Responsibilities

Embed with client teams to understand workflows and data, then design, validate, and deploy scalable enterprise AI applications and data pipelines. Operationalize and monitor solutions, resolve production issues, measure business impact, and support client adoption and knowledge transfer.

Requirements

Requires 10+ years of experience in data engineering, AI application development, and cloud data platforms, along with expertise in the listed engineering, AI, and deployment technologies. Candidates must have production MLOps/LLMOps experience, strong client-facing skills, and a relevant bachelor's degree; cloud, Kubernetes, AI engineering, or security certifications are a plus.

Skills

Python
PySpark
SQL
REST APIs
FastAPI
Git
Docker
Kubernetes
Airflow
MLflow
CI/CD
Claude
Prompt Engineering
Retrieval-Augmented Generation (RAG)
Vector Databases
MLOps/LLMOps

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
Artificial Intelligence
Cloud Data Platforms
Python
PySpark
SQL
REST APIs
FastAPI
Git
Docker
Kubernetes
Airflow
MLflow
CI/CD
Claude
Prompt Engineering
Retrieval-Augmented Generation (RAG)
Vector Databases
Agentic AI
MLOps
LLMOps
Data Pipelines
Batch Processing
Real-Time Data
Streaming Data
Cloud Platforms
Incident Resolution
BFSI
Client Discovery
Technical Presentations

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