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About the Role
We are looking for an AI Engineer to join our AI Engineering team focused on designing, building, and deploying intelligent AI assistants powered by Google Cloud Platform (GCP) and Vertex AI. You will work at the intersection of large language models, cloud infrastructure, and product engineering to deliver conversational AI experiences that drive real business value.
What You'll Do
Design and develop AI assistants using Vertex AI, including model selection, prompt engineering, grounding, retrieval-augmented generation (RAG), and agent orchestration.
Build intelligent, multi-step AI agents using the Google Agent Development Kit (ADK), designing agent architectures with tool use, sub-agents, callbacks, and session management for complex conversational workflows.
Develop and integrate applications using the Google Cloud AI/ML SDKs (Vertex AI SDK for Python, Google Gen AI SDK) for programmatic access to foundation models, embeddings, tuning, and prediction endpoints.
Design agentic architectures leveraging ADK's orchestration capabilities including sequential agents, loop agents, parallel agents, and custom agent hierarchies to handle multi-turn, multi-domain use cases.
Implement tool and function calling patterns within ADK agents, connecting them to enterprise APIs, databases, and external services to enable grounded, domain-specific responses.
Implement responsible AI practices including safety filters, bias detection, guardrails, hallucination mitigation, and content moderation.
Architect scalable, production-grade solutions using Vertex AI Agent Builder, Vertex AI Search, Cloud Run, Cloud Functions, Pub/Sub, BigQuery , and other GCP services.
Develop evaluation and monitoring frameworks to measure assistant quality, latency, cost, and user satisfaction in production — including ADK's built-in evaluation and tracing capabilities.
Collaborate with product managers, data engineers, and business stakeholders to translate requirements into AI-powered solutions.
Stay current with the rapidly evolving AI landscape, evaluate emerging models, tools, and techniques, and recommend adoption where appropriate .
Required Qualifications
Bachelor's or Master's degree in Computer Science , Machine Learning, Data Science, or a related field (or equivalent practical experience).
5 + years of experience in software engineering, with at least 2+ years focused on AI/ML or LLM-based agentic development.
Hands-on experience with Google Cloud Platform services, particularly BigQuery , Cloud Storage, Cloud Run, or Cloud Functions.
Hands-on e xperience with Vertex AI Agent Builder , Vertex AI Search, or Dialogflow CX.
Hands-on experience Experience with Google Cloud SDKs (Vertex AI SDK, Google Gen AI SDK) for model invocation, tuning, and deployment workflows.
Strong proficiency in Python and familiarity with ML/AI frameworks and libraries (e.g., LangChain , LlamaIndex , TensorFlow, PyTorch ).
Experience building agentic applications that leverage large language models (LLMs) — including prompt engineering, function calling, embeddings, and retrieval-augmented generation.
Solid understanding of API design , microservices architecture, and cloud-native development patterns.
Familiarity with version control (Git) , CI/CD pipelines, and Infrastructure as Code (e.g., Terraform, Cloud Build).
Preferred Qualifications
Google Cloud certifications (e.g., Professional Machine Learning Engineer, Professional Cloud Architect).
Exposure to multi-agent architectures , tool-use patterns, and agentic AI frameworks.
Experience with vector databases (e.g., Vertex AI Vector Search, AlloyDB , Pinecone, Weaviate ) and embedding-based retrieval.
Knowledge of MLOps practices including experiment tracking, model versioning, A/B testing, and monitoring in production.
Experience working in regulated industries (healthcare, finance, insurance) with an understanding of data…