Forward deployed engineer

Job Overview

Location: Paris, France

Responsibilities

• 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).
• 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.
• 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.
• 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.
• 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

• 10+ years in software engineering, infrastructure engineering, or applied ML engineering.
• 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).