Python developer with Gen AI/Agent (Python/GCP)

Description & Requirements

Who we are looking for

We are seeking for Python Developer with experience in building AI systems. This role requires deep expertise in Python, experience or exposure to leading agent orchestration frameworks (ADK, LangChain, LangGraph, etc.), and extensive experience leveraging Google Cloud Platform’s Vertex AI suite. You will be responsible for designing, deploying, and optimizing multi-step reasoning agents capable of advanced tool use, function calling, and managing diverse GenAI tasks across text and code generation domains. If you thrive on developing intelligent, goal-oriented AI applications, this is the role for you.

Who We Are Looking For

You are a pragmatic, solutions-focused developer with a robust background in software architecture and advanced AI concepts. You possess:

• Essential Python Proficiency: Exceptional expertise in Python is mandatory for ML development, API integration, and leveraging SDKs (Vertex AI SDK, OpenAI SDK).
• Agent Development Exposure: Experience in building and orchestrating complex AI workflows using state-of-the-art frameworks, specifically: LangChain, LangGraph, and ADK (Agent Development Kit).
• GenAI Task Experience: Practical experience developing solutions for core Generative AI tasks, including sophisticated function calling, code generation, classification, summarization, and prompt engineering for text generation.
• Grounding Techniques Knowledge: Understanding of grounding models using techniques like RAG (Retrieval Augmented Generation) to provide external, factual context for agents.
• GCP AI Ecosystem Familiarity: Strong working knowledge of key GCP GenAI and MLOps tools, including:
• Agent Tools: Direct experience utilizing Vertex AI Agent Builder for developing conversational and goal-oriented agents.
• Development Environments: A2A, Managed Compute Platform (MCP), Vertex AI Workbench/Notebooks for development.
• MLOps & Deployment: Vertex AI Pipelines for workflow orchestration.
• Model Management: Vertex AI Training for custom training, fine-tuning, and hyperparameter tuning.
• English proficiency
• EU work permit

While not strictly required, candidates possessing the following skills will have a distinct advantage:

• Graph Databases: Experience with graph databases (e.g., Neo4j) and their integration into LangGraph or RAG systems to manage complex relationships and contextual memory.
• Low-Latency Deployment: Experience optimizing GenAI applications for low-latency, high-throughput environments (e.g., using quantization or compiling models).
• Security & Compliance: Familiarity with data security principles related to PII/PHI handling within GenAI pipelines and implementing filtering mechanisms (e.g., DLP).
• Advanced MLOps: Deep experience with running production workloads on GKE or leveraging advanced features of Cloud Run for model serving.

Your future role

As a Generative AI Agent Developer, your primary responsibilities will include:

• Agent Orchestration: Design, develop, and deploy complex, multi-step AI agents using LangChain, LangGraph, and ADK, ensuring efficient state management and execution paths.
• Tool and Function Calling: Implement robust mechanisms for agents to utilize external APIs, custom Python functions, and specialized tools (Tool Use) to achieve complex goals and interact with enterprise systems.
• GCP Service Integration: Leverage the full suite of Vertex AI services (Agent Builder, Pipelines, Workbench) to manage the entire agent lifecycle, from testing to scalable production deployment.
• Model Integration and Customization: Integrate and manage various foundational models (GCP, open source) and utilize Vertex AI Training for model customization where required.
• Infrastructure & Networking: Utilize general GCP infrastructure knowledge, including Compute (Agent Engine, GKE, Cloud Run) for deployment, and adhere to networking standards (VPC, Load Balancing, Cloud DNS).
• Performance and Reliability: Implement rigorous testing and logging to monitor agent behavior, optimize token usage, ensure reasoning accuracy, and minimize potential hallucinations.
• Documentation & Best Practices: Document agent architectures, development processes, and promote reusable design patterns across the engineering team.