Forward Deployed Engineer, AI & Analytics

Location: Fully remote, USA and Latin America

Company

hirify.global is a global technology services company providing cloud architecture, infrastructure, migration, optimization, and continuous delivery services.

What you will do

• Take a Palantir Foundry analytics and predictive proof of concept through a full production build.
• Build data pipelines, ontology, application layers, governance, and observability.
• Work with client product and business teams to run discovery and translate policies and operating procedures into code and agent logic.
• Define and own adoption, delivery, or business KPI metrics with client leadership.
• Train and pair with client engineers while building, transferring capabilities through the engagement.
• Shape the roadmap from proof of concept to a production-grade product intended for market launch.

Requirements

• 6+ years of experience in software, data, or ML engineering with senior-level ownership of production systems.
• Hands-on production experience with Palantir Foundry, including Pipeline Builder, Ontology, and Workshop, or deep experience with a comparable platform such as Databricks or Snowflake.
• Production Python and SQL experience.
• Experience building LLM and agent systems involving retrieval, tool use, evaluation, and production deployment beyond demos.
• Consulting, solutions engineering, or client-embedded delivery experience.
• Clear written and spoken English with the ability to present to executives.

Nice to have

• Healthcare, staffing, workforce, or other regulated-industry experience.
• Experience as a Forward Deployed Engineer, solutions architect, or solutions engineer at a platform vendor or AI-native company.
• Track record of capability transfer through training, pairing, or engagement playbooks.
• TypeScript or Java experience.
• Palantir AIP experience.

Culture & Benefits

• Contract engagement through the Gigster network.
• Full-time dedication is expected.
• Initial engagement term is aligned with the production build, with likely extension as the pod model expands.
• Compensation is set according to experience and location.

Hiring process

• 30-minute recruiter interview.
• 30-minute technical interview.
• 60-minute first client interview.