Tech Lead & Solutions Architect (AI & Application Management)

Role Purpose

As the Tech Lead and Solutions Architect, you will serve as the bridge between complex business requirements and cutting-edge AI implementation. You will lead the design, development, and long-term management of enterprise applications, ensuring they are seamlessly integrated with Google Gemini to deliver autonomous, intelligent, and scalable user experiences.

Core Responsibilities

• Architectural Blueprinting: Design end-to-end scalable architectures for multi-tier applications, prioritizing modularity and high availability.

• AI Integration: Lead the architectural strategy for deploying applications into the Google Gemini/Vertex AI ecosystem, focusing on Model Garden, Tuning, and Grounding.

• Data Strategy: Design data pipelines and vector databases (e.g., Pinecone, BigQuery Vector Search) to provide real-time context to LLMs.

• Full-Stack Leadership: Oversee the development of applications using modern frameworks (LWC, React, Node.js, or Python) ensuring clean code and robust security.

• Lifecycle Management: Manage the entire Application Development Life Cycle (SDLC), from initial discovery and prototyping to production support and refactoring.

• DevOps & Deployment: Architect CI/CD pipelines using tools like GitHub Actions, Jenkins, or Google Cloud Build to ensure rapid, low-risk deployments.

• Mentorship: Act as the "North Star" for the engineering team, performing code reviews and enforcing technical standards.

• Vendor & Stakeholder Management: Collaborate with Product Owners and senior leadership to align the technical roadmap with business OKRs.

• Innovation: Stay at the forefront of Generative AI, identifying opportunities to replace legacy manual workflows with autonomous AI agents.

Technical Qualifications

• Platform Expertise: 10+ years of IT experience, with at least 4 years in a Solutions Architect or Tech Lead capacity.

• Google Cloud & Gemini: Deep proficiency in Google Cloud Platform (Google Cloud Platform), specifically Vertex AI, Gemini Pro/Flash, and Generative AI Studio.

• AI Methodologies: Demonstrated experience with RAG (Retrieval-Augmented Generation), Prompt Engineering, and fine-tuning LLMs for specific industry domains.

• Integration: Expert in API-led connectivity (REST/gRPC) and event-driven architecture.

• Security: Advanced knowledge of OAuth, JWT, and data privacy standards (HIPAA, GDPR) specifically regarding AI data processing.

Preferred Specialized Experience

• Experience in Healthcare or Fintech (highly regulated environments).

• Certifications: Google Professional Cloud Architect or Google Professional Machine Learning Engineer.

• Experience with Forward Deployment delivery models, working directly with end-users to iterate on AI-driven features.

Success Metrics

• AI Accuracy: Successful grounding of Gemini agents to achieve <5% hallucination rates.

• Velocity: Maintaining a high deployment frequency with a <10% change failure rate.

• Scalability: Systems designed to handle 2x current peak load without architectural redesign.