Senior Forward Deployed Engineer - AI & Kubernetes
$140,000–$160,000 year
On-siteToronto, Ontario, Canada
Job Summary
Embed with customer teams to understand business problems, workflows, and data systems, then translate ambiguous needs into clear technical scopes and production outcomes. Define success metrics, adoption paths, and measurable impact for each engagement before building and deploying AI applications, agentic workflows, and data pipelines on Kubernetes. Operate the platform across complex environments including cloud, hybrid, and air-gapped infrastructure while handling PagerDuty-based incident response and root-cause analysis. Partner with customer engineering, platform, and security teams to govern systems and turn specific implementations into reusable patterns and product feedback. This outcome-based role focuses on live, adopted workflows that improve customer operations and drive business results.
Required Qualifications
- 8+ years of experience across software, data, platform, infrastructure, or AI engineering roles
- 3+ years building LLM/AI applications such as RAG, agents, evaluations, workflow automation, or production AI systems
- 5+ years working with Kubernetes and cloud-native infrastructure in production environments
- Strong experience with major cloud platforms such as AWS, Azure, or GCP
- Strong data engineering background, including pipelines, orchestration, transformation, data quality, access controls, and production data workflows
- Experience with a modern data stack such as Spark, Airflow, Databricks, Snowflake, or similar
- Experience building or deploying AI, data, or automation solutions in highly regulated or operationally complex industries, such as financial services, government, healthcare, energy, agriculture, supply chain, or industrial operations
- Ability to apply AI to real-world operational data, such as sensor data, geospatial data, logistics data, ERP data, field operations data, or forecasting data
- Proficiency in at least one production programming language such as Python, Go, TypeScript, Java, or Scala
- Strong systems thinking across data, users, permissions, workflows, infrastructure, governance, and business processes
- Excellent customer-facing communication skills with engineers, operators, security teams, executives, and business owners
- Strong ownership mindset: you care about production rollout, adoption, reliability, operational handoff, and measurable impact
- Willingness to travel to customer sites as needed
Desired Qualifications
- Experience in a forward deployed, professional services, solutions architecture, customer engineering, field engineering, or technical consulting role
- Experience delivering outcome-based customer engagements where success was measured by adoption, operational improvement, or business impact
- Experience with data engineering, machine learning, or data science workflows, including feature engineering, model training, experimentation, evaluation, or production ML systems
- Experience with on-prem, private cloud, regulated, hybrid, or air-gapped deployments
- Experience with infrastructure-as-code and production operations
- Experience working with enterprise security, compliance, audit, access control, and governance requirements
- Experience integrating AI or data systems with enterprise applications, internal APIs, data platforms, or customer-specific operational tools
- Experience leading senior technical stakeholders through architecture reviews, security reviews, implementation planning, and production-readiness decisions
- Ability to identify repeatable product and service opportunities from customer-specific implementations
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