AI Platform Engineer

ActiveFence

. Build and maintain CI/CD pipelines supporting fast, reliable integration and deployment of GenAI systems across complex environments, including orchestration of multiple models/agents and MCP clients/servers .

Posted 10/5/2026full-timeRamat Gan • IsraelMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining CI/CD pipelines for GenAI systems, integrating AI solutions, and managing SQL data pipelines. Proficient in deploying generative AI models on AWS and automating workflows with a strong focus on collaboration and communication across teams.

Highest-signal resume keywords

CI/CD Pipeline DevelopmentAWS DeploymentPython ScriptingGenerative AI IntegrationSQL Data Pipeline Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

CI/CD Pipeline DevelopmentPython ScriptingSQL Database ManagementGenerative AI Model DeploymentInfrastructure as Code (IaC)Linux Systems AdministrationMicroservices ArchitectureData Pipeline AutomationCommand-Line Tool DevelopmentAgentic Framework Integration

Soft Skills

Cross-Functional CommunicationCollaborationDevOps Mindset

Tools & Technologies

AWSDockerHuggingFacePrometheusGrafanaSlurm

Certifications & Qualifications

B.Sc. in Computer ScienceM.Sc. in Computer Science (Nice-to-Have)

Industry Keywords

Generative AILarge Language ModelsData CurationModel EvaluationAdversarial PromptsHigh-Performance SystemsCloud Cost OptimizationInfrastructure Security

Tech Stack

Tools & technologies

AWSCloudGrafanaLinuxMicroservicesPrometheusPythonSQL

About the role

Key responsibilities & impact

Build and maintain CI/CD pipelines supporting fast, reliable integration and deployment of GenAI systems across complex environments, including orchestration of multiple models/agents and MCP clients/servers
Develop, deploy, and integrate AI solutions and agentic frameworks, including command-line tools and interactive notebooks for research and workflow automation
Build and manage SQL data pipelines and databases for large-scale data handling
Automate data collection and curation, including adversarial prompts for model red-teaming, to support AI training and evaluation
Integrate research into production by implementing generative AI advancements, including white-box LLM development
Integrate different models and agents and develop evaluation frameworks such as agentic reasoning tests within automated workflows
Integrate dockerized environments, such as Harbor format, into internal training frameworks while optimizing reset/statefulness semantics, concurrency, and throughput ceilings
Deploy GenAI models from repositories such as HuggingFace onto AWS
Connect models to data pipelines and automate end-to-end prompt generation, labeling, and response evaluation processes

Requirements

What you’ll need

B.Sc. in Computer Science, Computer Engineering, or a related field (or equivalent hands-on experience)
5+ years of experience delivering large-scale, high-performance systems on AWS, with an emphasis on orchestrating data pipelines and AI workflows in production environments
Expertise in scripting and automation using Python and shell; proven ability to write clean IaC and CI/CD pipelines
Strong understanding of Linux systems, networking, and distributed system design
Ability to break down monolithic systems into scalable, loosely coupled services and microservices
Strong cross-functional communication and collaboration skills, with a DevOps mindset to drive best practices across teams
Hands-on experience deploying and integrating generative AI models (e.g., large language models or other AI/ML models) in production
Hands-on experience deploying MCP clients/servers and integrating them into automated AI workflows
Nice-to-have: M.Sc. in Computer Science, Computer Engineering, or a related field
Nice-to-have: Experience with large-scale cluster management or HPC scheduling tools (e.g., Slurm)
Nice-to-have: Knowledge of advanced observability and monitoring tools such as Prometheus and Grafana
Nice-to-have: Experience with cloud cost optimization (FinOps concepts) and exposure to infrastructure security tools or configuration management for compliance
Nice-to-have: Hands-on experience working with A2A protocol