Forward Deployed Engineer

HRConnects; Toronto; Hybrid; CAD 140,000 - 180,000; Full time

Job summary

HRConnects is seeking a Forward Deployed Engineer to embed with client teams and drive AI-powered data solutions. You will design scalable data pipelines, deploy AI applications across cloud platforms, and implement MLOps/LLMOps for secure production environments, while measuring impact and enabling knowledge transfer.

Located in Toronto, this hybrid role starts as soon as possible and offers a competitive salary range with enterprise-grade collaboration.

Qualifications

• 10+ years in data engineering, AI app development, and cloud data platforms.
• Proficiency in Python, PySpark, SQL, REST APIs, FastAPI, Git, Docker, Kubernetes, Airflow, MLflow, CI/CD.
• Experience with Claude, prompt engineering, RAG, vector databases, and MLOps/LLMOps in production.

Responsibilities

• Embed with client teams to map workflows, data, and decision points.
• Validate and harden solutions using real client data and APIs.
• Design and deploy enterprise AI/data apps across cloud platforms.
• Build and manage batch, real-time, and streaming data pipelines.
• Implement MLOps/LLMOps for lifecycle and deployment automation.
• Conduct structured error analysis and prioritize improvements.
• Resolve blockers related to data access, security, and infra.
• Monitor post-launch performance, ensure reliability, and stabilize.
• Collaborate with clients to track KPIs and transfer knowledge.

Skills

Data engineering

Python

PySpark

SQL

REST APIs

Git

Docker

Kubernetes

Airflow

MLflow

CI/CD

MLOps/LLMOps

Claude

Prompt engineering

Vector databases

Education

Bachelor’s degree

Tools

Docker

Kubernetes

Airflow

MLflow

Git

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