Junior Python AI Developer

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Junior Python AI Developer

Hybrid in United States of America: Sunnyvale

Python.Core& 5 others

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Were looking for a motivated Junior Python AI Developer to help build, deploy, and optimize AI agents in production environments. Youll work closely with senior engineers and customers to bring intelligent automation to life using ADK and Python. This is a hybrid role requiring 3 days onsite in Sunnyvale, CA. If youre passionate about AI, love solving technical problems, and want to grow your career at the forefront of agent-based systems - we want to hear from you.

Req# 1060117865

Responsibilities

• Build & Deploy AI Agents: Design, develop, test, and ship agent workflows using Agent Development Kit (ADK) and Python in production environments

• Develop Integrations: Connect the platform with customer systems, databases, and cloud services using Python and REST APIs

• Test & Improve Performance: Evaluate agent behavior against real-world scenarios, investigate failures, and iterate on code/configuration to improve reliability

• Troubleshoot Issues: Debug integration, data, configuration, and agent-behavior problems across development and production environments

• Collaborate with Customers & Teams: Participate in technical discussions, document requirements, and translate business needs into clear implementation tasks

• Grow with Emerging AI Tech: Build hands-on expertise in AI agents, with potential exposure to MCP and RAG pipelines

Requirements

• Experience: Up to 2 years in software engineering, implementation engineering, solutions engineering, or a related technical role (strong internship/project experience considered)

• Python Proficiency: Solid ability to write, debug, test, and maintain production-quality Python code (OOP, error handling, logging, testing)

• ADK Experience: Hands-on experience building AI agents using Agent Development Kit (ADK) is required

• Integration Skills: Working knowledge of REST APIs, JSON, authentication methods, and third-party system integrations

• LLM Fundamentals: Understanding of prompts, context management, tool usage, model limitations, and response evaluation

• Communication & Ownership: Clear technical communication skills, proactive task management, and attention to documentation and code quality

Nice to have

• Experience with Model Context Protocol (MCP) or Retrieval-Augmented Generation (RAG) pipelines, including vector databases and embeddings

• Exposure to cloud platforms (GCP, AWS, or Azure), Docker, or CI/CD pipelines

• Previous experience in a customer-facing engineering, consulting, or support role