Senior Forward Deployed Engineer AI & Kubernetes

Tech Talent International (TTI) supplies technical talent to a variety of clients ranging from Fortune 100/500/1000 companies to startups small and mid-sized organizations in Canada/US. We are currently hiring Senior Forward Deployed Engineer - AI & Kubernetes for our client in the Toronto area which specializes in OS and secure toolkitdevelopment for AI and data stacks.

Role: Senior Forward Deployed Engineer - AI & Kubernetes

Type: Fulltime Perm

Salary Range: $140000 - $160000 as base salary depending on overall experience stock options benefits unlimited vacation days

Location: Onsite - downtown Toronto ON Canada

We are seeking a senior Forward Deployed Engineer to join our clients Platform team and workdirectly with strategic customers to turn business problems into production AI and data systems.
This is a highly technical customer-facing role for someone who can move between customerconversations system design data engineering AI application development Kubernetes cloudinfrastructure and production support.
In this role you will own the path from discovery to production: understanding the customersworkflows and data designing the solution building and deploying it and helpingthe customer adopt it in real operations.

This is an outcome-based engineering role. Asuccessful engagement is not a demo a prototype or an installation. It is a live governedadopted workflow that improves how the customer operates and is tied to a clear businessresult.

Responsibilities
Embed with customer teams to understand business problems workflows data systemsconstraints and success metrics.

Define what success looks like for each engagement including the target outcomeadoption path production boundary and measurable impact.

Translate ambiguous customer needs into clear technical scopes architecturesimplementation plans and production outcomes.

Build and deploy AI and data applications on the platform including agentic workflowsRAG systems data pipelines integrations evaluations and operational automation.

Design and implement production data workflows across enterprise environmentsincluding ingestion transformation orchestration data quality access control andobservability.

Deploy and operate the platformin complex customer environments including cloudhybrid on-prem private cloud and air-gapped infrastructure.

Work hands-on with Kubernetes containers networking storage identity secretsobservability and production troubleshooting.

Partner with customer engineering platform data and security teams to get systemslive governed adopted and measurable.

Participate in PagerDuty-based production support for customer deployments includingincident response escalation root-cause analysis and follow-up remediation.

Turn customer-specific work into reusable patterns playbooks templates and productfeedback for the company

Qualifications
8 years of experience across software data platform infrastructure or AI engineeringroles including:
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 productionenvironments.
Strong experience with major cloud platforms such as AWS Azure or GCP.
Strong data engineering background including pipelines orchestration transformationdata quality access controls and production data workflows.
Experience with a modern data stack such as Spark Airflow Databricks Snowflake orsimilar.
Experience building or deploying AI data or automation solutions in highly regulated oroperationally complex industries such as financial services government healthcareenergy agriculture supply chain or industrial operations.
Ability to apply AI to real-world operational data such as sensor data geospatial datalogistics data ERP data field operations data or forecasting data.
Proficiency in at least one production programming language such as Python GoTypeScript Java or Scala.
Strong systems thinking across data users permissions workflows infrastructuregovernance and business processes.
Excellent customer-facing communication skills with engineers operators securityteams executives and business owners.
Strong ownership mindset: you care about production rollout adoption reliabilityoperational handoff and measurable impact.
Willingness to travel to customer sites as needed.

A Plus
Experience in a forward deployed professional services solutions architecturecustomer engineering field engineering or technical consulting role.
Experience delivering outcome-based customer engagements where success wasmeasured by adoption operational improvement or business impact.
Experience with data engineering machine learning or data science workflowsincluding feature engineering model training experimentation evaluation or productionML 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 andgovernance requirements.
Experience integrating AI or data systems with enterprise applications internal APIsdata platforms or customer-specific operational tools.
Experience leading senior technical stakeholders through architecture reviews securityreviews implementation planning and production-readiness decisions.
Ability to identify repeatable product and service opportunities from customer-specificimplementations.