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Publié il y a 3 jours

Forward Deployed Engineer

@ 3Pillar

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Canada

Temps plein10+ ans d’expérience
Postuler
SIGNAUX

En bref

3Pillar is hiring a Forward Deployed AI Engineer to embed with client teams for the length of an engagement, identify high-leverage workflows, and build, deploy and iterate production AI integrations. The role combines hands-on Python and cloud engineering with client-facing communication and reusable pattern development. The posting states full-time employment; schedule.

Responsabilités :

  • Embed with client teams and identify high-leverage workflows
  • Build, test and harden production integrations against client systems
  • Own deployment, monitoring and user adoption
  • Run build-measure-learn cycles with client stakeholders
  • Contribute reusable patterns and connectors to HelixAI

Résumé des exigences :

  • 10+ years of software engineering experience
  • 2+ years shipping production AI systems
  • Strong Python with LangChain and/or LangGraph
  • Hands-on AWS experience including Bedrock, Lambda and S3
  • Experience in forward-deployed or field engineering roles

Outils et compétences

PythonLangChainLangGraphAWS BedrockAWS LambdaAWS S3
Source et dates

Type de source non vérifié · apify-linkedin

Date publiée par l’employeur
2026-11-07
Première détection par notre source
2026-11-10
Mise à jour du dossier source
2026-11-10

Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.

Détails du poste

NiveauSenior
DomaineDéveloppement logiciel
Expérience10 an(s) et plus
Heures par semaine40 (estimé)
Milieu de travailChez les clients, Bureau
Exigences physiquesphysique faible · ordinateur élevé
LanguesEnglish

Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.

Postuler
ChampValeur
Offres sur Jobsy1
Description

Forward Deployed AI Engineer

Responsibilities

Embed on-site or in tight virtual proximity with client teams for the life of the engagement, and identify the highest-leverage workflow to build first, then the next.

Build, test, and harden production-grade integrations (APIs, MCP connectors, data pipelines) directly against client systems.

Own the last mile: deployment, monitoring, user adoption, and iteration based on real usage, not just proof-of-concept handoff.

Run build-measure-learn cycles with client stakeholders, translating ambiguous business problems into scoped technical solutions.

Act as a trusted technical counterpart to client engineering leads and executive sponsors; communicate tradeoffs in business terms.

Contribute reusable patterns, connectors, and accelerators back to HelixAI (Atlas, Nexus, AIRE) for future engagements.

What Good Judgment Looks Like Here

Simplifies complexity fast: turns a messy client environment into a clear, sequenced plan without waiting for a full spec.

Decides soundly under pressure and owns the call, rather than escalating everything or waiting to be told what to do.

Writes production code directly when that's what moves the engagement forward. This isn't an architecture-only or advisory seat.

Spots risk early and adapts, rather than discovering a blocker after it's already cost the engagement time.

Turns what's learned in the field into something reusable, not a one-off fix for one client.

Qualifications

10+ years of professional software engineering experience, including 2+ years building and shipping production AI systems (not prototypes).

Depth in retrieval architecture (RAG), agent orchestration/tool-use patterns, and evaluation pipeline design.

Strong Python proficiency, with production experience using LangChain and/or LangGraph.

Hands-on AWS experience across core compute, storage, and AI/ML services (e.g., Bedrock, Lambda, S3).

Demonstrated ability to work independently and ambiguously in a client-facing, field environment.

Hands-on experience integrating with enterprise systems via APIs, webhooks, or connector frameworks.

Prior experience in a forward-deployed, solutions engineering, or field engineering role.

Nice to Have

Experience with agentic SDLC methodologies and AI-assisted development tooling (Claude Code, Cursor, GitHub Copilot, or equivalents).

Familiarity with RBAC, audit logging, and governance requirements for regulated or enterprise clients.

Background in a specific vertical (financial services, asset management, healthcare, etc.).

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