Agentic AI developer
100% Remote
Hire Type: Contract
Skill Proficiency: | |||
Skill | Proficiency Level | Category | Weight (%) |
Python Programming | Expert | Core | 15% |
LangGraph | Advanced | Core | 15% |
Agent-to-Agent (A2A) Protocols | Advanced | Core | 15% |
Prompt Engineering & Optimization | Advanced | Core | 10% |
AI Evaluation Techniques | Advanced | Core | 10% |
LangChain / LLM Frameworks | Intermediate | Core | 10% |
Docker & Containerization | Intermediate | Core | 10% |
Kubernetes | Intermediate | Preferred | 10% |
CI/CD Pipelines | Intermediate | Preferred | 10% |
Cloud Deployment (AWS/Azure/GCP) | Intermediate | Preferred | 10% |
Key Responsibilities for AI Developer:
• Design and implement scalable architectures for AI agents using LangGraph and the A2A protocol
• Build robust evaluation pipelines to benchmark agent behavior, quality, and performance (e.g., using AI evals, custom metrics)
• Fine-tune and optimize system prompts and agent configurations for specific tasks and workflows
• Ensure scalability, fault tolerance, and performance tuning of agent systems in production environments
• Manage deployment workflows using tools like Docker, Kubernetes, and CI/CD pipelines
• Implement logging, monitoring, and feedback loops to continuously improve agent performance and reliability
Requirements:
• Hands-on experience deploying and scaling AI agents in production environments
• Strong familiarity with LangGraph and Agent-to-Agent (A2A) communication protocols
• Experience with AI evaluation techniques, prompt iteration workflows, and outcome benchmarking
• Proven ability to fine-tune system prompts and agent behaviors for robustness and alignment
• Proficiency in Python and experience working with modern ML/LLM frameworks (LangChain, OpenAI, etc.)
• Experience with containerization (Docker), CI/CD, and cloud-based deployment infrastructure
• Hands-on experience deploying and scaling AI agents in production environments
• Strong familiarity with LangGraph and Agent-to-Agent (A2A) communication protocols
• Experience with AI evaluation techniques, prompt iteration workflows, and outcome benchmarking
• Proven ability to fine-tune system prompts and agent behaviors for robustness and alignment
• Proficiency in Python and experience working with modern ML/LLM frameworks (LangChain, OpenAI, etc.)
• Experience with containerization (Docker), CI/CD, and cloud-based deployment infrastructure
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