Agentic AI Engineer

About the Role

We're seeking an experienced Agentic AI Engineer to design, develop, and deploy production-grade AI agent systems that solve complex, real-world problems. You'll build sophisticated multi-agent workflows using LangGraph and large language models (LLMs), crafting reusable components that enable autonomous decision-making, task orchestration, and agent-to-agent collaboration.

This is a hands-on engineering role where you'll bridge cutting-edge AI capabilities with robust software engineering practices. You'll write clean, testable Python code, engineer reliable prompts that control agent behavior, and build CI/CD pipelines that automate deployment of intelligent systems.

Key Responsibilities

AI Agent Development

• Design and develop reusable agentic AI workflows using LangGraph and state-of-the-art LLMs
• Build multi-agent systems where autonomous agents collaborate to complete complex tasks
• Implement Agent-to-Agent (A2A) communication patterns for coordinated problem-solving
• Create modular, composable agent components that can be reused across different workflows

Prompt Engineering & LLM Control

• Craft and refine clear, effective prompts for diverse tasks and agent behaviors
• Optimize prompts for reliability, consistency, and output quality
• Develop prompt templates and frameworks that ensure predictable agent performance
• Implement prompt versioning and testing strategies

Software Engineering & Testing

• Write clean, production-grade Python code following best practices and design patterns
• Develop comprehensive pytest unit tests to ensure agent reliability and correctness
• Build integration tests for multi-agent workflows and A2A interactions
• Maintain high code quality through code reviews and documentation

DevOps & Automation

• Use Git for version control in a collaborative team environment
• Design and maintain GitLab CI/CD pipelines to automate build, test, and deployment processes
• Implement monitoring and observability for agent systems in production
• Automate agent workflow testing and validation

Required Qualifications

Must Have

Strong prompt engineering skills for LLM control and output reliability

• Demonstrated ability to craft prompts that produce consistent, high-quality results
• Experience debugging and optimizing LLM behavior through prompt refinement
• Understanding of prompt engineering patterns (few-shot learning, chain-of-thought, etc.)

Proven ability to build and maintain LangGraph workflows and reusable components

• Hands-on experience building stateful agent workflows with LangGraph
• Track record of creating modular, reusable agent components
• Experience with graph-based agent orchestration patterns

Proficiency in Python, including writing pytest unit tests

• Strong Python programming skills with emphasis on clean, maintainable code
• Experience writing comprehensive test suites with pytest
• Familiarity with Python async/await patterns and concurrency

Git version control and GitLab CI/CD experience

• Comfortable using Git in a team environment (branching, merging, pull requests)
• Experience building and maintaining GitLab CI/CD pipelines
• Knowledge of automated testing, deployment, and release strategies

Good to Have

Hands-on experience in agentic AI system design and orchestration

• Experience designing multi-agent architectures for complex workflows
• Understanding of agent planning, reasoning, and decision-making patterns
• Familiarity with agent evaluation and benchmarking methodologies

Solid understanding of A2A orchestration principles

• Experience with agent communication protocols and message passing
• Knowledge of coordination patterns (delegation, collaboration, negotiation)
• Understanding of when to use single vs. multi-agent approaches