AI Deployment Specialist (SPX Express)

Deployment Architecture & Strategy: Scope, architect, and lead the deployment of AI-based applications and large language models (LLMs) across diverse operational workflows. End-to-End Integration: Manage the full lifecycle of AI model integration, wrapping machine learning models into robust, scalable APIs and integrating them seamlessly into existing full-stack production systems.

MLOps & CI/CD Pipelines: Design and maintain automated CI/CD pipelines for machine learning models, ensuring seamless versioning, testing, and deployment without downtime. Infrastructure Management: Utilize Linux-based environments and containerization orchestration (Docker, Kubernetes) to provision, scale, and manage AI application infrastructure.

Monitoring & Optimization: Implement comprehensive telemetry to monitor model performance, detect data/concept drift, and track system latency. Optimize deployed systems to meet strict service-level agreements (SLAs) and safety standards. Cross-Functional Collaboration: Partner actively with Data Scientists to transition models into production-ready code, and with product teams to identify new opportunities for AI integration.

Education

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a closely related field. Experience: 3+ years of hands-on experience deploying, managing, and scaling AI/ML applications in live production environments. Core Languages & Frameworks: Advanced proficiency in Python and modern web frameworks (e.

g. FastAPI, Node.js). Full-stack development experience to build internal tooling and integration endpoints. Systems & OS: Mandatory proficiency in Linux systems administration, bash scripting, and resource management (CPU/GPU allocation). Cloud & DevOps: Strong practical experience with major cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD tools (GitHub Actions, GitLab CI, or Jenkins).

API & Integration: Deep understanding of RESTful APIs, gRPC, and microservices architecture. Infrastructure as Code (IaC): Experience using Terraform, Cloud. Formation, or Ansible to provision deployment infrastructure. LLM Experience: Direct experience deploying and optimizing generative AI models, working with frameworks like Lang.

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