Lead Agentic AI Architect - Boston

Perficient•Boston, MA

4d•$81,978 - $178,090•Onsite

About The Position

Perficient currently has a career opportunity for a Lead Agentic AI Architect-Google Vertex & Gemini onsite in Boston, MA. As Lead Agentic AI Architect, you will be part of Perficient's Cloud/API Practice developing best in class experiences for Perficient customers. You will provide technology direction, ensure project implementation, and utilize technology research to innovate, integrate, and manage technology solutions. You will also significantly contribute to identifying best-fit architectural solutions for one or more projects; you will collaborate with some of the best talent in the industry to create and implement innovative, high-quality solutions, participate in sales and various pursuits focused on our clients' business needs. This role requires being onsite in the Boston area 5 days a week. Perficient is always looking for the best and brightest talent and we need you! We’re a quickly-growing, global digital consulting leader, and we’re transforming the world’s largest enterprises and biggest brands. You’ll work with the latest technologies, expand your skills, and become a part of our global community of talented, diverse, and knowledgeable colleagues.

Requirements

Gemini & Agentic AI: deeply hands-on with Vertex AI Agents and Gemini 1.5. You must understand how to build and deploy Gemini Connectors to ground models in enterprise data.
Low-No Code Velocity: Leverage Vertex AI Agent Builder to rapidly prototype and deploy agentic workflows, bypassing traditional heavy-code bottlenecks when speed is critical.
Fast-Paced Agile: drive delivery in a high-velocity environment. You thrive in 2-week sprints and can pivot strategy based on real-time feedback.
Rigorous SDLC: enforce software development lifecycle best practices for AI—ensuring that "Action Fabrics" are tested, versioned, and scalable (CI/CD for Data & AI).
Industrialization: Move POCs to production by prioritizing stability, security, and scale over novelty.
Google Cloud Native: 7+ years of deep experience within the GCP ecosystem (BigQuery, Dataflow, Looker, Vertex AI).
GenAI Practitioner: Proven experience building RAG (Retrieval Augmented Generation) architectures and grounding LLMs (specifically Gemini) with enterprise data.
The "Connector" Mindset: Experience building custom connectors and integrations that allow AI agents to read from and write back to enterprise systems (ERP, CRM).
Agile Leadership: Certified Scrum Master or equivalent practical experience leading agile teams in a consulting or product environment.
Communication: Exceptional ability to command a room, manage stakeholder expectations, and simplify the complex.
Experience with prompt engineering, RAG, or agentic AI architectures
Experience with agentic frameworks in practice (langchain, n8n, pydantic, etc)
Demonstrated ability to leverage AI tools to enhance productivity, streamline workflows, and support data-informed task execution.
A solid understanding of AI capabilities and limitations including ethical considerations is expected.

Nice To Haves

Familiarity with AI-enhanced platforms is a plus.

Responsibilities

Articulate complex Data & AI strategies to C-level executives, translating technical "action fabrics" into business value.
Lead high-velocity squads while remaining willing to jump into the console to debug, prototype, or architect solutions.
Define and drive the AI and platform architecture vision, establishing best practices for building secure, scalable, and high-performance systems on GCP.
Design and implement AI/ML capabilities across the platform, including data ingestion, model lifecycle management, and real-time monitoring to deliver actionable insights.
Continuously assess new AI/ML tools and trends to ensure the platform remains innovative and aligned with strategic business goals.
Act as a key liaison between engineering, product, and business stakeholders, translating business needs into actionable architectural designs and ensuring cohesive execution.
Define and enforce architectural standards, toolsets, and integration frameworks.
Direct initiatives to monitor and enhance platform performance and cost-efficiency using advanced platform tools, ensuring high availability and scalability.
Guide and support engineering teams, fostering a culture of innovation, collaboration, and continuous learning within the architecture function.
Implementing guardrails, optimizing for costs, and evaluating model performances.