Machine Learning Engineer

Avensys Consulting; Paris; Sur place; EUR 100 000 - 125 000; Plein temps

Résumé du poste

A leading technology consulting firm is seeking a Senior Forward Deployment Engineer in Paris to lead the implementation of AI platforms within large-scale enterprises. This role demands deep engineering expertise, especially in deployment engineering and DevOps, as well as hands-on experience in machine learning operationalization. The ideal candidate will architect, design, and deploy solutions ensuring high performance and reliability. Strong leadership skills are essential for collaborating with technical teams and stakeholders.

Qualifications

• Extensive experience in deployment engineering within complex enterprise environments.
• Strong understanding of security best practices and compliance standards.
• Proven ability to create and optimize integration pipelines and automation scripts.

Responsabilités

• Lead technical deployment of AI platforms in enterprise environments.
• Implement and customize platform components to meet specific use cases.
• Architect deployment solutions for reliability, security, and compliance.

Connaissances

Deployment Engineering

DevOps capabilities

ML operationalization skills

Architectural thinking

Automation with CI/CD

Outils

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

1. Deployment Engineering

• 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‑div investigations, post‑mortems, performance tuning, and scalability assessments.