Agentic AI Developer

Qualifications

• 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
• 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

Responsibilities

• Design and implement scalable architecture 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
• Proven ability to fine-tune system prompts and agent behaviors for robustness and alignment

Full Description

One of our prime clients is hiring Agentic AI Developer for San Jose, CA/ RTP, North Carolina.

Role: Agentic AI Developer (Fulltime Permanent Role)

Location: San Jose, CA/ RTP, North Carolina

Interview: Video Interview

Description:

Key Responsibilities

• Design and implement scalable architecture 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