Artificial Intelligence
il y a 6 jours
Role: Forward Deployment Engineer
Location: Paris (Onsite Working)
Job Type: 03-06 Months Contract and Extendable
French-speaking
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 in software engineering, infrastructure engineering, or applied ML engineering.
- Strong proficiency in any
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.
-
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