9 meilleures offres (devops / architecte gcp, paris, a8) (à pourvoir)

Forward Deployed Engineers - Python - Paris
Photon —Paris (75)
- Azure
- DevOps
- JavaScript
Applied AI Engineer, ML Infrastructure Engineer / Devops - EMEA
Mistral AI —Paris (75)
- Temps plein
- Azure
- DevOps
- Anglais
Architecte Cloud (H/F)
Synapsys —Paris (75)
- Temps plein
- Azure
Postuler rapidement
Java - DevOps Architect
Avanade —Issy-les-Moulineaux (92)
- Azure
- DevOps
- Java
Infrastructure Solution Architect - EMEA
Mistral AI —Paris (75)
- Temps plein
- Azure
- DevOps
- Intelligence artificielle
Développeur Full stack .NET/ React Confirmé F/H
Viseo —Boulogne-Billancourt (92)
- Temps plein
- Azure
Solution Architect Business Information
Coface —Bois-Colombes (92)
- Temps plein
- Azure
- Anglais
- Master
Postuler rapidement
Senior Cloud Engineer - Cracow/Paris/Lyon
LumApps —Paris (75)
- Télétravail partiel
- Azure
- Anglais
- Intelligence artificielle
Senior Backend Engineer Java / AI (H/F)
ALTEN —Boulogne-Billancourt (92)
- Temps plein
- Azure
- DevOps
- Ingénierie
Postuler rapidement

More jobs to discover

Find similar jobs that you might be interested in.

Applied AI Engineer, ML Infrastructure Engineer / Devops - EMEA
Mistral AI —Paris (75)
You hold strong communication skills with an ability to explain complex technical concepts in simple terms to technical and non-technical audiences.
Applied AI Engineer, Site Reliability Engineer - EMEA
Mistral AI —Paris (75)
Lead CVE response across the fleet, ship supply-chain integrity controls (SBOM, signed images, provenance), co-page with InfoSec on security incidents, enforce…

En vous connectant à votre compte, vous acceptez les Conditions d'utilisation de SimplyHired et consentez à notre Politique relative aux cookies et Politique de confidentialité.

Key Responsibilities

1. Deployment Engineering

Lead the end-to-end technical implementation of the Agentic platform in enterprise environments.
Design and build robust integration pipelines , connecting customer data sources, APIs, and systems of record to the platform.
Deploy and scale machine learning models in production, ensuring performance, reliability, and monitoring.
Automate deployments using CI/CD pipelines, infrastructure-as-code, and container orchestration (Docker, Kubernetes).

2. AI Platform Integration & Optimization

Implement custom extensions, SDKs, and APIs to adapt the platform to customer-specific use cases.
Build tools, scripts, and microservices to handle data preprocessing, feature engineering, and real-time inference .
Optimize model serving, caching, and resource allocation for low-latency, high-throughput environments.

3. Reliability, Security & Compliance

Architect solutions that meet enterprise-grade standards for resilience, observability, and scalability.
Ensure deployments adhere to security best practices (encryption, identity management, network security).
Navigate compliance requirements such as SOC2, HIPAA, GDPR , and customer-specific regulatory constraints.

4. Engineering Leadership & Technical Escalation

Serve as the senior technical lead on customer deployments, resolving complex engineering challenges.
Partner closely with customer engineering teams to embed the platform into production workflows.
Provide critical field feedback to product and core engineering teams on performance, scaling, and enterprise integration needs.

5. Enablement & Knowledge Sharing

Create reusable deployment templates, automation scripts, and playbooks to accelerate future projects.
Mentor other Forward Deploy Engineers on advanced deployment patterns, DevOps practices, and ML systems engineering.

Qualifications

Engineering Expertise
- 10+ years in software engineering, infrastructure engineering, or applied ML engineering.
- Strong proficiency in Python , TypeScript/JavaScript , or other backend languages.
- Experience deploying systems on cloud platforms (AWS, GCP, Azure) using Kubernetes and serverless frameworks.
- Deep understanding of API design, distributed systems, and data engineering workflows .
- Hands-on experience operationalizing ML models in production (TensorFlow, PyTorch, Hugging Face, or custom inference engines).
DevOps & Infrastructure
- Strong background in CI/CD pipelines, infrastructure as code (Terraform, Helm, Ansible) .
- Skilled in setting up monitoring, observability, and alerting (Prometheus, Grafana, ELK, Datadog).
- Familiarity with performance profiling, scaling strategies, and SRE principles.
Security & Compliance Awareness
- Knowledge of enterprise SaaS security models (SSO, RBAC, encryption, API security).
- Experience working in environments subject to compliance frameworks (SOC2, HIPAA, GDPR).
Soft Skills
- Excellent debugging and problem-solving in high-pressure deployment environments .
- Strong communication with technical stakeholders (engineering teams, architects, CTOs).
- Comfort working in fast-moving, ambiguous situations with minimal guidance.