Forward Deployed Engineer
- Toronto, ON, Canada
Full-time
Applications open in a new tab.
Job details
- Job type
- Full-time
- Apply by
- Experience
- 10+ years of experience in data engineering, AI application development, and cloud data platforms
- Posting language
- English
- Posted
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
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
- 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
- BFSI industry experience
- Additional certifications in cloud, security, or architecture
More about this job
Responsibilities
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.
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.
Skills
- Python
- PySpark
- SQL
- REST APIs
- FastAPI
- Git
- Docker
- Kubernetes
- Airflow
- MLflow
- CI/CD
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- Vector Databases
- MLOps/LLMOps
- Cloud Data Platforms
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
- Consulting
Keywords
- Data Engineering
- AI Application Development
- 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 Processing
- Streaming Data
- Cloud Platforms
- Production Monitoring
- Error Analysis
- BFSI
- Client Discovery