Forward Deployed Engineer (Data, ML & AI)

This position requires a Forward Deployed Engineer (FDE) specializing in Data, Machine Learning, and AI to embed directly within customer environments. You will serve as the primary technical authority transforming complex data challenges and operational bottlenecks into production-grade data pipelines, machine learning systems, and agentic AI solutions. The role is heavily customer-facing: you will work alongside client business units and engineering teams to rapidly assess legacy data estates, build scalable modern data platforms, and deploy custom GenAI/LLM workflows that drive measurable business velocity. This is a high-ownership, hands-on role where you will leverage Spec-Driven Development (SDD) and AI-assisted workflows to build, refactor, and demonstrate immediate value directly where the customer operates.

Responsibilities

Deploy directly into customer environments to understand domain logic, underlying data pipelines, and architectural constraints; Own end-to-end delivery from data discovery and schema design to pipeline deployment and model integration; Partner with client business leaders to evaluate legacy technology estates and lead refactoring into modern lakehouses, event-driven streaming systems, and scalable vector/graph databases; Apply a spec-first engineering workflow using Generative AI tools to write structured specifications that instruct AI agents to generate production data models, PySpark jobs, data pipelines, and test suites; Architect and deploy GenAI workflows, RAG pipelines, and autonomous AI agents using frameworks such as LangChain, LlamaIndex, or DSPy; Establish evaluation frameworks (Evals) for model accuracy, latency, and hallucination control; Build, deploy, and maintain ML training and inference pipelines using MLflow, Kubeflow, Airflow, or Dagster; Ensure CI/CD for models and data workflows; Design and audit production code across Python, SQL, Scala, Go, Rust, or TypeScript; Establish reusable agentic development patterns, MLOps practices, data reliability frameworks, and SDD methodologies for customer teams

Requirements

10+ years of experience as a Principal Data Engineer, Lead ML Engineer, or Enterprise Data Architect building and scaling distributed data and ML platforms; Strong executive presence and communication skills to interface with technical teams and business stakeholders under pressure; Mastery of distributed computing (AWS Glue, Apache Spark, Databricks), modern data warehouses (Redshift, Snowflake), modeling tools (dbt), and data orchestration (Airflow, etc.); Hands-on experience fine-tuning, evaluating, and deploying LLMs, embedding models, and vector stores; Advanced proficiency in Python and complex SQL, plus fluency in at least two other languages used in modern backend/data systems (e.g., Scala, Go, Rust, TypeScript); Demonstrated skill in using natural language and structured specs to guide AI tools (Claude Code, Cursor, Copilot) in generating data pipelines, schemas, and API adapters; Hands-on experience with cloud-native data services on AWS or Azure, containerization (Docker, Kubernetes), and Infrastructure as Code (Terraform); Comfort with occasional travel to customer sites as needed

Nice to have

Prior experience in a Forward Deployed Engineer, Data Architect, or technical consulting/professional services role; Experience migrating legacy, on-premise data warehouses or legacy Hadoop estates to modern cloud lakehouses; Deep understanding of data governance, security compliance (HIPAA, SOC2, GDPR), and privacy-preserving machine learning

Benefits

Competitive compensation package; Retirement Savings Matching Program (RRSP); Access to the latest tech; Partnership with Perkopolis Discounts; Remote first work environment; Flexible work hours & location; Paid parental leave options; Employer paid health & dental premiums; GreenShield+ Counselling Mental Health; $500 in Health Care Spending Account annually; Peer recognition rewards