Forward deployed engineer

LanceSoft, Inc.; Paris; Sur place; EUR 80 000 - 110 000; Plein temps

Résumé du poste

A technology solutions provider in Paris is seeking a Senior Technical Lead to oversee the implementation of their Agentic platform in enterprise environments. The ideal candidate will have over 10 years of experience in software engineering and machine learning, with expertise in deploying systems on cloud platforms, including AWS and Azure, using Kubernetes. Responsibilities include leading integration efforts, ensuring compliance with security regulations, and mentoring teams. This role promises significant impact on technical deployments and solutions within client workflows.

Qualifications

• 10+ years in software engineering, infrastructure engineering, or applied ML engineering.
• Experience deploying systems on cloud platforms using Kubernetes.
• Understanding of API design and distributed systems.
• Operationalizing ML models in production.
• Strong background in CI/CD pipelines and infrastructure as code.

Responsabilités

• Lead technical implementation of the Agentic platform.
• Design robust integration pipelines connecting data sources.
• Deploy and scale ML models in production.
• Automate deployments using CI/CD pipelines.
• Ensure deployments adhere to security best practices.

Connaissances

Software engineering

Infrastructure engineering

Machine learning engineering

API design

Kubernetes

CI/CD pipelines

Data engineering workflows

Observability

Outils

AWS

GCP

Azure

Docker

Terraform

Prometheus

Grafana

Datadog

Description du poste

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).