

TrueSkilla
Native AI Lead
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a "Native AI Lead" on a W2 contract for 100% remote work. Required skills include 8–10+ years in software engineering, cloud-native systems, and AI/LLM production experience. Proficiency in Python or Java is essential.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
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🗓️ - Date
April 17, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#Logging #Monitoring #Docker #AI (Artificial Intelligence) #Cloud #Infrastructure as Code (IaC) #Kubernetes #Scala #Observability #Microservices #Containers #Deployment #Python #Java #Terraform
Role description
Role: Native AI Lead
Type: W2 Contract Only
Location: 100% Remote
Overview
Seeking a hands-on AI Native Software Lead to design, build, and deploy production-grade AI-driven systems within enterprise environments. The role focuses on implementing agent-based workflows, integrating AI platforms, and delivering scalable cloud-native solutions.
Responsibilities
• AI Agent Engineering
• Design and implement AI agents, including:
• Retrieval (RAG)
• Orchestration workflows
• Tool/function invocation
• Policy-based routing
• Build evaluation frameworks for accuracy, latency, and reliability
• Implement observability and monitoring for agent lifecycle
• AI Platform Integration
• Integrate with AI providers (e.g., OpenAI, Anthropic, Google Vertex, open-source models)
• Build abstraction layers to support multi-model and multi-provider architectures
• Optimize model usage for performance, cost, and latency
• Cloud-Native Development
• Develop scalable services using:
• Microservices architecture
• Containers (Docker, Kubernetes)
• Serverless and event-driven patterns
• Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm)
• Ensure production readiness, logging, monitoring, and fault tolerance
• Application Development
• Build and deploy AI-powered applications aligned to business workflows
• Integrate AI systems into existing enterprise platforms and APIs
• Develop backend services and APIs supporting agent workflows
• Testing & Performance
• Define and execute test strategies for AI systems
• Measure system performance (latency, throughput, accuracy, cost)
• Debug and optimize production systems
Required Skills & Experience
• 8–10+ years of software engineering experience
• Strong experience with cloud-native systems (APIs, microservices, containers, serverless)
• Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration)
• Proficiency in Python, Java, or similar backend languages
Experience with:
• CI/CD pipelines
• Infrastructure as code
• Monitoring and observability tools
• Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar).
Preferred Experience
• Experience with agent frameworks (e.g., LangGraph, AutoGen, CrewAI)
• Experience designing multi-agent or distributed AI systems
• Familiarity with enterprise-scale system integration
• Experience optimizing AI workloads for cost and performance
• Scope & Expectations (Contractor-Specific)
• 100% hands-on engineering role (no people management)
• Deliver production-quality code and deployments
• Work within existing architecture and engineering standards
• Collaborate with client and internal engineering teams as needed
• Participate in technical design discussions (implementation-focused)
Role: Native AI Lead
Type: W2 Contract Only
Location: 100% Remote
Overview
Seeking a hands-on AI Native Software Lead to design, build, and deploy production-grade AI-driven systems within enterprise environments. The role focuses on implementing agent-based workflows, integrating AI platforms, and delivering scalable cloud-native solutions.
Responsibilities
• AI Agent Engineering
• Design and implement AI agents, including:
• Retrieval (RAG)
• Orchestration workflows
• Tool/function invocation
• Policy-based routing
• Build evaluation frameworks for accuracy, latency, and reliability
• Implement observability and monitoring for agent lifecycle
• AI Platform Integration
• Integrate with AI providers (e.g., OpenAI, Anthropic, Google Vertex, open-source models)
• Build abstraction layers to support multi-model and multi-provider architectures
• Optimize model usage for performance, cost, and latency
• Cloud-Native Development
• Develop scalable services using:
• Microservices architecture
• Containers (Docker, Kubernetes)
• Serverless and event-driven patterns
• Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm)
• Ensure production readiness, logging, monitoring, and fault tolerance
• Application Development
• Build and deploy AI-powered applications aligned to business workflows
• Integrate AI systems into existing enterprise platforms and APIs
• Develop backend services and APIs supporting agent workflows
• Testing & Performance
• Define and execute test strategies for AI systems
• Measure system performance (latency, throughput, accuracy, cost)
• Debug and optimize production systems
Required Skills & Experience
• 8–10+ years of software engineering experience
• Strong experience with cloud-native systems (APIs, microservices, containers, serverless)
• Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration)
• Proficiency in Python, Java, or similar backend languages
Experience with:
• CI/CD pipelines
• Infrastructure as code
• Monitoring and observability tools
• Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar).
Preferred Experience
• Experience with agent frameworks (e.g., LangGraph, AutoGen, CrewAI)
• Experience designing multi-agent or distributed AI systems
• Familiarity with enterprise-scale system integration
• Experience optimizing AI workloads for cost and performance
• Scope & Expectations (Contractor-Specific)
• 100% hands-on engineering role (no people management)
• Deliver production-quality code and deployments
• Work within existing architecture and engineering standards
• Collaborate with client and internal engineering teams as needed
• Participate in technical design discussions (implementation-focused)






