Forward Deployed Engineer (OpenAI Platform)

Carbon60•Toronto, ON

27d•CA$160,000 - CA$200,000•Remote

About The Position

This position requires a Forward Deployed Engineer (FDE) specializing in Data, Machine Learning, and OpenAI technologies to embed directly within customer environments. You will serve as the primary technical authority transforming legacy data estates and operational bottlenecks into production-grade data pipelines, OpenAI-powered agentic workflows, and enterprise AI solutions. The role is heavily customer-facing: you will work alongside client business units and engineering teams to rapidly build modern data platforms and deploy custom OpenAI architectures (leveraging o-series reasoning models, Assistants API, and Azure OpenAI Service). This is a high-ownership, hands-on role where you will leverage Spec-Driven Development (SDD) and AI-assisted workflows to drive immediate enterprise value directly where the customer operates.

Requirements

10+ Years of Experience: Proven track record as a Principal Data Engineer, Lead ML/AI Architect, or Enterprise Solutions Architect scaling distributed data systems.
Mastery of OpenAI Platform APIs, Azure OpenAI Service, Fine-Tuning, Structured Outputs, Function Calling, Realtime API, and OpenAI Vector Stores/Embeddings.
Strong executive presence to interface directly with client C-suite stakeholders and technical engineering leads.
Demonstrated proficiency using structured natural language specs to guide AI tools in auto-generating ETL jobs, test suites, and schema contracts.
Comfort with occasional travel (once/quarter to customer sites).

Nice To Haves

Prior experience in a Software/Data Engineer or technical consulting role within enterprise AI.
Direct experience migrating on-premises legacy data systems to Azure OpenAI cloud architectures.
Deep understanding of AI governance, content safety filtering, and private endpoint deployments for OpenAI workloads.

Responsibilities

Deploy directly into customer environments to audit domain logic and data pipelines.
Own end-to-end delivery—from data discovery to OpenAI API integration and model fine-tuning—building trust as the customer's lead technical partner.
Partner with client leadership to refactor legacy SQL databases, monolithic ETL jobs, or unmaintained data warehouses into modern lakehouses optimized for low-latency retrieval and OpenAI embeddings.
Apply a "spec-first" engineering workflow using OpenAI-powered tools (Cursor, Copilot). Write structured specifications (schemas, function definitions, transformations) to auto-generate production PySpark jobs, data models, and API wrappers.
Architect production-grade solution pipelines utilizing OpenAI Assistants API, Structured Outputs, Function Calling/Tool Use, and multi-agent orchestration SDKs.
Build advanced RAG architectures using OpenAI Embeddings and specialized vector engines.
Establish robust evaluation frameworks (OpenAI Evals) to measure accuracy, latency, and hallucination control.
Implement aggressive token budget management, prompt caching, batch processing via OpenAI Batch API, and circuit breakers.
Train customer engineering teams on OpenAI best practices, Azure OpenAI security standards (VNet, RBAC, Data Privacy), SDD methodologies, and maintainable MLOps patterns.