Forward Deployed Engineer, AI & Analytics

Accountabilities

• • Take an existing analytics and predictive proof of concept into production, covering data pipelines, ontology, application development, governance, and observability.
  • Work directly with business and product stakeholders to understand operational challenges, conduct discovery sessions, and translate policies and procedures into working software and AI-driven solutions.
  • Build and integrate production-grade applications, pipelines, dashboards, agent workflows, and supporting technical components.
  • Develop AI and LLM-powered capabilities involving retrieval, tool use, evaluation, and agent-based workflows, moving solutions beyond proof-of-concept stages.
  • Define meaningful adoption, delivery, or business-performance metrics with client business leaders and take ownership of reporting against those outcomes.
  • Shape the roadmap from an initial proof of concept toward a scalable, production-ready product that can support broader business adoption.
  • Collaborate closely with client engineers while delivering solutions, providing hands-on training, pairing, documentation, and knowledge transfer.
  • Help establish repeatable technical practices, playbooks, and workflows that enable client teams to maintain and extend the solutions independently.
  • Partner with product and business teams to challenge inefficient processes, understand the underlying business context, and identify opportunities for better technical solutions.
  • Present technical recommendations, progress, and outcomes to senior stakeholders and executives.
  • Contribute as part of a multidisciplinary engineering pod and help establish AI-focused delivery capabilities across additional business units.

  Requirements

  • 6+ years of experience in software engineering, data engineering, ML engineering, or a closely related discipline, with senior-level ownership of production systems.
  • Hands-on production experience with Palantir Foundry, including technologies such as Pipeline Builder, Ontology, and Workshop; experience with AIP is a strong advantage.
  • Alternatively, deep experience with comparable data and AI platforms such as Databricks or Snowflake, combined with the ability to ramp quickly on new platforms.
  • Strong production experience with Python and SQL; TypeScript or Java experience is a plus.
  • Demonstrated experience building and deploying LLM- and agent-based solutions involving retrieval, tool use, evaluation, and productionization.
  • Experience in consulting, solutions engineering, forward-deployed engineering, or client-embedded technical delivery.
  • Ability to lead discovery sessions, facilitate technical workshops, train engineering teams, and communicate recommendations effectively to senior technical and business stakeholders.
  • Strong written and spoken English, with confidence presenting technical concepts and defending recommendations in front of executives.
  • Demonstrated ability to work as a technical generalist, independently building integrations, pipelines, application components, dashboards, and supporting tooling rather than waiting for narrowly defined tickets.
  • Proven experience owning solutions end-to-end and being accountable for measurable outcomes such as adoption, time to production, or business KPIs.
  • Strong business curiosity and the ability to understand why an existing process works the way it does before translating it into technology.
  • Healthcare, staffing, workforce, or other regulated-industry experience is a plus.
  • Previous experience as a Forward Deployed Engineer, Solutions Architect, or Solutions Engineer at a platform vendor or AI-focused organization is advantageous.
  • Experience transferring technical capabilities through training, pairing, documentation, or engagement-specific playbooks is preferred.

  Benefits

  • Full-time dedication expected through a contract engagement.
  • Initial contract term aligned with the production build, with extension opportunities likely as the delivery model expands.
  • Competitive compensation based on experience and location.
  • Fully remote working environment.
  • Opportunity to work directly with business and product leaders on high-impact AI and analytics initiatives.
  • Exposure to production AI, LLMs, agents, data platforms, and enterprise application development.
  • Opportunity to build solutions that deliver measurable business outcomes rather than isolated technical prototypes.
  • Hands-on collaboration with client engineering teams and opportunities to develop technical leadership and knowledge-transfer skills.
  • Potential for continued engagement as AI-focused engineering pods expand across additional business units.