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Job Summary
We are seeking a Senior Generative AI Engineer to design, develop, and deploy enterprise-scale Agentic AI and Generative AI solutions. This role is responsible for building production-ready AI applications using Google Agent Development Kit (ADK), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, and LangGraph while driving AI platform strategy, engineering standards, and operational excellence. The ideal candidate will have strong software engineering expertise, experience building AI-enabled enterprise applications, and a deep understanding of modern agentic architectures.
Key Responsibilities
• Design, develop, and deploy production-ready AI agents using Google Agent Development Kit (ADK).
• Build multi-agent AI solutions leveraging Google ADK orchestration, tool ecosystems, and deployment frameworks.
• Design and develop Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic architectures.
• Build production-grade AI workflows using LangChain and LangGraph.
• Develop AI-enabled services and integrate AI capabilities into enterprise applications and business workflows.
• Design secure tool execution patterns supporting service-to-service communication, least privilege access, auditability, and enterprise governance.
• Implement enterprise agent-to-agent communication patterns and Model Context Protocol (MCP) tool integrations where applicable.
• Evaluate AI technologies, orchestration frameworks, and model architectures to address complex business requirements.
• Troubleshoot, optimize, and resolve issues across AI models, orchestration frameworks, and production services.
• Develop AI lifecycle capabilities including evaluation frameworks, quality monitoring, model performance tracking, and model drift detection.
• Design, code, test, debug, document, and deploy AI services across development, testing, and production environments.
• Contribute to enterprise AI platform strategy, engineering standards, and operational readiness initiatives.
• Collaborate with architects, engineers, product teams, and business stakeholders to deliver scalable AI solutions.
• Mentor engineers, provide technical leadership, and serve as an escalation point for complex technical challenges.
• Ensure AI solutions comply with organizational security, governance, compliance, and risk management standards.
Required Qualifications
• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical field, or equivalent practical experience.
• Strong hands-on experience developing enterprise applications using Python and/or Java.
• Experience developing AI agents using Google Agent Development Kit (ADK).
• Strong understanding of Large Language Models (LLMs), transformer architectures, and conversational AI.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.
• Hands-on experience with LangChain and LangGraph frameworks.
• Experience building and deploying production-grade Agentic AI applications.
• Experience implementing Model Context Protocol (MCP) integrations and enterprise AI orchestration patterns.
• Experience with MLOps practices, AI model evaluation, quality monitoring, and model lifecycle management.
• Experience building and managing vector databases for semantic search and knowledge retrieval.
• Experience implementing vector search, embeddings, and enterprise retrieval architectures.
• Experience working with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Experience with containerized application deployment and cloud-native architectures.
• Experience implementing CI/CD pipelines and automated software deployment processes.
• Strong analytical, debugging, troubleshooting, and problem-solving skills.
• Excellent verbal and written communication skills with the ability to explain complex AI concepts to technical and business stakeholders.
Preferred Qualifications
• Experience implementing human-in-the-loop workflows, policy guardrails, and AI safety controls for agentic systems.
• Experience with event streaming and messaging technologies such as Apache Kafka.
• Experience with Test-Driven Development (TDD), Behavior-Driven Development (BDD), and modern software engineering practices.
• Experience working in financial services or other regulated industries.
• Experience designing enterprise AI governance, monitoring, and operational frameworks.
• Experience leading AI engineering teams and mentoring software engineers.