Forward Deployment Engineer

il y a 1 semaine

Paris, Île-de-France Photon Temps plein

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