Google ADK GenAI Developer
Argyll Info Tech Inc.,
Charlotte, NC, United States
5 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Working hours
Regular working hours
Job source
Tech stack
LangGraph Framework
Application Programming Interfaces (APIs)
Agile Methodology
Artificial Intelligence
Databases
Software Debugging
DevOps
Python (Programming Language)
Scrum Methodology
Search Technologies
Software Engineering
Systems Integration
+28 more
Pinecone
Google Cloud
Enterprise Software Applications
Cloud Platform System
Spring Cloud
LangChain
Retrieval-Augmented Generation
Large Language Models
Multi-Agent Systems
Prompt Engineering
Google Vertex AI
Model Validation
Generative AI
Agentic-AI
Vertex AI Vector Search
Containerization
AI Platforms
Optimization Algorithms
CrewAI
Weaviate
AutoGen
Milvus
Virtual Agents
Google Gemini
Restful APIs
Semantic Kernel
Docker
ChromaDB
Job description
- Implement AI agent orchestration frameworks to support multi-agent collaboration and task execution.
- Integrate AI services with enterprise platforms, APIs, databases, and external applications.
- Create and optimize prompts, tools, and workflows to improve AI agent effectiveness and response quality.
- Collaborate with AI architects and business teams to translate requirements into scalable AI solutions.
- Develop secure and production-ready AI applications following enterprise architecture and governance standards.
- Monitor and optimize AI model performance through evaluation, testing, and continuous improvement.
- Support deployment and operationalization of AI applications within cloud environments.
- Implement observability, security, and compliance controls for AI systems and agent ecosystems.
- Participate actively in Agile ceremonies including sprint planning, backlog refinement, standups, and retrospectives.
- Troubleshoot and resolve AI application issues during development, testing, and production support phases.
- Create technical documentation including architecture diagrams, design specifications, and operational procedures.
- Evaluate emerging AI technologies and frameworks and recommend adoption opportunities.
- Partner with cross-functional teams to ensure successful delivery of AI transformation initiatives.
Requirements
- Minimum 5+ years of software engineering experience with at least 2+ years focused on Generative AI and Agentic AI development.
- Hands-on experience developing AI applications using Google Agent Development Kit (ADK).
- Strong expertise in Python programming and AI application development.
- Experience working with Google Vertex AI, Gemini Models, AI Studio, and Google Cloud Platform (GCP).
- Hands-on experience building AI Agents, Multi-Agent Systems, and Agentic Workflows.
- Strong knowledge of Retrieval-Augmented Generation (RAG), embeddings, semantic search, and knowledge retrieval systems.
- Experience working with Vector Databases such as Pinecone, Weaviate, Chroma, Milvus, or Vertex AI Vector Search.
- Knowledge of AI orchestration frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
- Experience integrating AI solutions with REST APIs, enterprise applications, databases, and third-party services.
- Strong understanding of prompt engineering, model evaluation, and LLM optimization techniques.
- Experience developing and deploying cloud-native applications in GCP environments.
- Knowledge of containerization technologies such as Docker and Kubernetes.
- Experience with CI/CD pipelines and DevOps practices.
- Understanding of AI governance, responsible AI, security, and compliance requirements.
- Experience working in Agile/Scrum environments.
- Strong analytical, debugging, and problem-solving capabilities.
- Excellent communication and stakeholder management skills.
- Banking and Financial Services domain experience is preferred.
Requirements:
- Design and develop enterprise-grade AI agents using Google ADK and Gemini models.
- Build and maintain Agentic AI solutions capable of reasoning, planning, and executing complex business workflows.
- Develop RAG-based applications integrating enterprise knowledge repositories and data sources.
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Prepare application
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