Machine Learning Engineer

Avensys Consulting

Paris

Sur place

EUR 70 000 - 90 000

Plein temps

14 jours+
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Résumé du poste

A technology consulting firm in Paris is seeking a highly experienced Senior Forward Deployment Engineer to lead the technical implementation of AI platforms in large-scale environments. This role requires deep engineering expertise and strong DevOps capabilities for seamless deployments. Responsibilities include designing integration pipelines, optimizing AI model serving, and ensuring compliance with security standards. The ideal candidate will take ownership of complex engineering challenges while collaborating with enterprise teams for smooth production integration.

Qualifications

  • Deep engineering expertise in deploying AI systems.
  • Experience with CI/CD pipelines and infrastructure-as-code.
  • Strong knowledge of security and compliance standards.

Responsabilités

  • Lead the technical deployment of the Agentic AI platform.
  • Architect and design integration pipelines for enterprise applications.
  • Ensure compliance with industry-specific regulatory requirements.
  • Act as the senior technical lead on customer deployments.

Connaissances

Engineering expertise
DevOps capabilities
Machine Learning operationalization
Automation scripting
Container orchestration (Docker, Kubernetes)

Description du poste

We are seeking a highly experienced Senior Forward Deployment Engineer to lead the technical implementation, integration, and production rollout of advanced Agentic AI platforms in large-scale enterprise environments. This role combines deep engineering expertise, strong DevOps capabilities, and hands‑on ML operationalization skills to ensure seamless deployment, scalability, and reliability of AI‑driven systems.

This is a senior engineering position requiring ownership, architectural thinking, and the ability to collaborate directly with enterprise technical teams, product engineering, and leadership stakeholders.

Key Responsibilities
  • Lead the end-to-end technical deployment of the Agentic AI platform across complex enterprise environments.
  • Architect, design, and build integration pipelines connecting customer systems, APIs, databases, and enterprise applications.
  • Deploy, operate, and scale machine learning models in production with a focus on performance, reliability, and monitoring.
  • Automate end-to-end deployments using CI/CD pipelines, infrastructure-as-code, and container orchestration tools (Docker, Kubernetes).
  • Ensure smooth rollout, versioning, and updates across staging, pre-prod, and production environments.
2. AI Platform Integration & Optimization
  • Implement and customize platform components, SDKs, APIs, extensions, and microservices to meet customer-specific use cases.
  • Build tools and automation scripts for data preprocessing, feature engineering, batch/real-time inference, and model lifecycle operations.
  • Optimize model serving layers for low latency, high throughput, and efficient resource utilization.
  • Improve caching, load balancing, and inference pipelines to support mission‑critical AI workloads.
3. Reliability, Security & Compliance
  • Architect deployment solutions aligned with enterprise‑grade reliability, resilience, and observability standards.
  • Implement best practices for security, including encryption, IAM, secret management, and network policies.
  • Ensure platform deployments comply with SOC2, HIPAA, GDPR, and industry‑specific regulatory requirements.
  • Set up robust monitoring, logging, and alerting frameworks for proactive issue resolution.
4. Engineering Leadership & Technical Escalation
  • Act as the senior technical lead on customer deployments, owning resolution of complex engineering challenges.
  • Work directly with customer engineering, infrastructure, and architecture teams to embed the platform into core production workflows.
  • Provide critical field insights and feedback to the product engineering team for continuous platform improvement.
  • Lead deep‑diving technical investigations, post-mortems, performance tuning, and scalability assessments.
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