Location: Toronto, ON
Work Model: Onsite
Job Type: Full Time

Primary Skills

Python/PySpark, Claude & Agentic AI, SQL/APIs & MLOps

Job Description

We are seeking an experienced Forward deployed Engineer with solid expertise in Data Engineering, AI application development, LLM integration, and MLOps. The ideal candidate will work closely with client stakeholders, business users, and engineering teams to design, build, deploy, and operationalize enterprise‑grade AI and data solutions.

This role combines hands‑on software engineering, data discovery, solution architecture, rapid prototyping, and production ownership. The engineer will leverage Python, PySpark, SQL, Claude, REST APIs, RAG, and modern cloud data platforms to deliver scalable, secure, and measurable business outcomes.

Key Responsibilities

- Design, develop, and deploy enterprise‑grade AI and data applications using Python, PySpark, Claude, SQL, REST APIs, and cloud data platforms.
- Collaborate with business and technology stakeholders to understand requirements, workflows, data landscapes, and delivery objectives.
- Define solution architecture, implementation roadmaps, and technical approaches aligned with business goals.
- Design and implement scalable batch, real‑time, and streaming data pipelines.
- Build Claude‑powered applications, copilots, and AI workflows using prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns.




- Perform data discovery and profiling using Python, pandas, PySpark, and SQL to assess data quality, coverage, volumes, and business‑process gaps.
- Develop rapid prototypes using real client data and APIs, validate solutions with business users, and iteratively prepare them for production.
- Implement and maintain CI/CD pipelines, automated testing, deployment automation, and infrastructure practices.
- Operationalize MLOps and LLMOps capabilities, including MLflow, model lifecycle management, monitoring, rollback, and production support.
- Apply responsible AI, security, privacy, governance, traceability, and human oversight controls.
- Troubleshoot data, API, identity, network, infrastructure, and deployment issues in collaboration with client teams.
- Create evaluation datasets, measure AI/LLM quality, conduct error analysis, and prioritize improvements.
- Monitor production services, resolve incidents, optimize performance and latency, and manage post‑go‑live stabilization.
- Measure user adoption and business impact using agreed KPIs, dashboards, and executive summaries.
- Develop reusable accelerators, reference architectures, engineering standards, runbooks, and implementation playbooks.
- Conduct client workshops, technical demonstrations, solution assessments, effort estimation, and RFP support.
- Mentor engineering teams and provide structured knowledge

📌 Forward deployed Engineer-6 (Toronto)
🏢 Realign
📍 Toronto