25 meilleures offres (full stack backend engineer, remote) (à pourvoir)

Software Engineer, GTM AI - Python
Telnyx —Montréal, QC
- Télétravail
- Salesforce
- Conception de systèmes
- SQL
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Mozilla —À domicile
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Xero —Vancouver, BC
- De 186 000 $ à 236 000 $ par an
- Temps plein
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- Java
- Bases de données
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22 h
Engineering Team Lead
ScriptRunner —Toronto, ON
- Temps plein
- Expérience en gestion
- Java
- AWS
- Congés payés
- Heures d'arrivée et de départ flexibles
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Lead Java Developer
EPAM Systems, Inc. —À domicile
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- DevOps
- Java
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- Assurance Invalidité
5 h
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- Temps plein
- Télétravail
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nesto —À domicile
- Temps plein
- Télétravail
- Développement logiciel
- Compétences en communication
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il y a 8 jours
AI Product Engineer, Games
Puzzle Cats —À domicile
- Temps plein
- Télétravail
- JavaScript
- Scriptage
- Intelligence artificielle
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HelloFresh —Toronto, ON
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- Intelligence artificielle
- Python
- Assurance Dentaire
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Staff Software Engineer - AI Platform
CaptivateIQ —Toronto, ON
- De 181 289 $ à 285 272 $ par an
- Temps plein
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- Python
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Principal Software Architect
Carbon60 —Toronto, ON
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- Temps plein
- Télétravail
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- Java
- Assurance Dentaire
- Heures d'arrivée et de départ flexibles
il y a 4 jours
Principal Software Engineer - Platform, Identity and BI
Calabrio —Toronto, ON
- Sur appel
- Télétravail
- DevOps
- Java
- SQL
- Jours fériés payés
- Assurance Invalidité
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Sr. Application Engineer
Red Thread Innovations —À domicile
- Temps plein
- Télétravail
- Azure
- Informatique
- DevOps
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Engineering Tech Lead
TTEC Digital —Toronto, ON
- Temps plein
- Télétravail
- APIs
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- Python
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il y a 11 jours
Product Owner, DevX and AI — Shop Builder
Xsolla —À domicile
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Senior React Native Developer
Speer Technologies Inc. —À domicile
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Engineering Manager, Maps & Search - CANADA (Remote)
Luxury Presence —À domicile
- Temps plein
- Télétravail
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TTEC Digital —Toronto, ON
- Temps plein
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Asana —Vancouver, BC
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Alpaca —À domicile
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Je souhaite recevoir la dernière alerte emploi pour les postes de ce type : full stack backend engineer (remote)

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Software Engineer, GTM AI - Python

Telnyx
Montréal, QC

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Détails du poste

Compétences

CI/CD
Droit
Kubernetes
Salesforce
Slack
Conception de systèmes
SQL
Docker
APIs
Flask
Intelligence artificielle
Évaluation de modèle
Python

Description complète du poste

About Telnyx

Telnyx is an industry leader that's not just imagining the future of global connectivity—we're building it. From architecting and amplifying the reach of a private, global, multi-cloud IP network, to bringing hyperlocal edge technology right to your fingertips through intuitive APIs, we're shaping a new era of seamless interconnection between people, devices, and applications.

We're driven by a desire to transform and modernize what's antiquated, automate the manual, and solve real-world problems through innovative connectivity solutions. As a testament to our success, we're proud to stand as a financially stable and profitable company. Our robust profitability allows us not only to invest in pioneering technologies but also to foster an environment of continuous learning and growth for our team.

Our collective vision is a world where borderless connectivity fuels limitless innovation. By joining us, you can be part of laying the foundations for this interconnected future. We're currently seeking passionate individuals who are excited about the opportunity to contribute to an industry-shaping company while growing their own skills and careers.

About the Team

The RevOps team owns the systems layer, operations & automation that supports Telnyx's growth engine. Historically, that meant administering GTM tools used by humans: Salesforce, marketing automation, enrichment vendors, routing, campaign workflows, reporting, and vendor integrations.

That operating model is changing. Telnyx is increasingly building AI agents and automation that interact directly with the GTM stack. The systems team now needs to support both human-facing workflows and bot-facing infrastructure: clean data, reliable integrations, durable automations, documented process, and scalable operating patterns.

About the Role

We're looking for a Software Engineer who builds and operates the AI-native backend systems powering our go-to-market motion. You'll design multi-agent architectures, build reliable integrations across complex business systems, and own services end-to-end from prototype through production.

The systems you build orchestrate LLM-powered agents that handle real business workflows — qualifying leads, generating emails, routing meetings, enriching contacts, and managing outbound campaigns. These are stateful, multi-step agent systems running on Kubernetes that make decisions, call tools, and interact with external APIs under real constraints: rate limits, token budgets, cost targets, and data quality issues.

You'll partner with Engineering Leads and Technical Product Managers to understand the problem space, then translate those problems into well-architected, observable, and maintainable software. This isn't prompt engineering and it isn't gluing together SaaS tools - it's systems engineering with AI as a core primitive.

This is a hands-on builder role with high ownership. You'll make architectural decisions, ship iteratively, debug production issues, and care deeply about what happens after code merges.

Responsibilities

Design and build multi-agent AI systems in Python that handle complex, multi-step business workflows - qualification, email generation, routing, enrichment, and outbound orchestration
Architect model-agnostic abstraction layers that decouple business logic from LLM providers, enabling flexibility across Claude, GPT, and open-source models
Build and operate backend services (FastAPI/Flask) deployed on Kubernetes with CI/CD, managing the full lifecycle from deployment configuration to production reliability
Design tool-use patterns for AI agents - structured function calling, multi-step reasoning, state management across conversation turns, and graceful handling of model failures
Build integrations across external systems (CRM, enrichment APIs, outreach platforms, Slack) with proper error handling, retries, rate limiting, and data contracts
Instrument and monitor AI systems in production — build observability into agent behavior, track success rates, detect regressions, and debug non-deterministic failures
Design and run experiments (A/B tests, prompt variations, model comparisons) with proper evaluation infrastructure to measure what's actually working

Requirements

2+ years of software engineering experience building backend services in Python
Production experience building multi-step AI agent systems — stateful workflows where models make decisions, call tools, and operate across multiple turns, not single-shot API wrappers
Strong understanding of LLM internals as they affect system design: context window management, token budgets, cost/latency/capability tradeoffs across models, structured outputs, and strategies for handling hallucination and refusals
Experience testing and evaluating non-deterministic AI systems — you understand that assert output == expected doesn't work and have built or used alternatives
Solid software architecture fundamentals: API design, state management, fault tolerance, and graceful degradation when upstream services fail
Production experience with containerized deployments (Docker, Kubernetes) and CI/CD pipelines
Experience integrating with external APIs at scale — auth flows, rate limiting, retries, data normalization, and managing the operational complexity of multiple third-party dependencies
Proficiency with SQL and data systems for building targeting, enrichment, and analytics pipelines
Built observability into production systems — structured logging, tracing, alerting, and monitoring that you actually use to debug issues
High ownership: you deploy your own code, investigate your own incidents, and close the loop between what you shipped and how it performs

Nice to Have

Experience with specific GTM/RevOps systems (Salesforce, Apollo, Lusha, enrichment providers) or similar complex business platforms
Background in growth engineering, marketing automation, or revenue operations tooling
Experience with Slack bot development or conversational AI interfaces
Contributions to or experience with open-source AI agent frameworks
Familiarity with ArgoCD, StatefulSets, or Kubernetes operations beyond basic deployments

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Je souhaite recevoir la dernière alerte emploi pour les postes de ce type : full stack backend engineer (remote)

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