Field Solutions Architect, Applied AI, Google CloudChicago Staffing • United States
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Field Solutions Architect, Applied AI, Google Cloud
Chicago Staffing
- United States
- United States
Über
This opportunity will remain online based on business needs which may be before or after February 2, 2026. This role may also be located in our Playa Vista, CA campus. "Applicants in the County of Los Angeles: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act." Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act. In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: Health, dental, vision, life, disability insurance Retirement Benefits: 401(k) with company match Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year Note: By applying to this position you will have an opportunity to share your preferred working location from the following: New York, NY, USA; Atlanta, GA, USA; Austin, TX, USA; Boulder, CO, USA; Chicago, IL, USA; Addison, TX, USA; Kirkland, WA, USA; Miami, FL, USA; Mountain View, CA, USA; Los Angeles, CA, USA; Reston, VA, USA; Seattle, WA, USA; San Francisco, CA, USA; Washington D.C., DC, USA. Minimum Qualifications
Bachelor's degree in Computer Science or equivalent practical experience in Software Engineering, Site Reliability Engineering (SRE), or DevOps. 6 years of experience in Python and experience architecting scalable AI systems on cloud platforms. Experience deploying conversational agents using code-based frameworks and build in real-time with customers utilizing modern generative AI tools. Experience deploying resources via Terraform or similar tools to automate the setup of agents, functions, and networking. Experience building full-stack applications (not just scripts) that interact with enterprise IT infrastructures also in developing and driving customer projects forward in a timely manner. Preferred Qualifications
Master's or PhD in AI, Computer Science, or a related technical field. Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google's ADK) and patterns like ReAct, self-reflection, and hierarchical delegation. Experience debugging Agent logic (ReAct loops, Chain of Thought) and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time. Experience connecting agents to enterprise knowledge bases and optimizing Retrieval-augmented generation (RAG) chunking to prevent hallucinations. Ability to troubleshoot live, high-traffic systems during critical windows. Ability to travel up to 50% of the time. About The Job
As a Field Solutions Architect (FSA) in Applied AI, you are the "Agent Engineer" and the primary delivery arm for our customers' most critical AI initiatives. You take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering life-cycle, including the transition from "Art of the Possible" to real-world business value and scalable, secure AI systems. This is a high-travel, high-impact role focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers at their sites. Your role requires an understanding of software engineering, MLOps, and cloud infrastructure. Responsibilities
Lead the development of Conversational AI and CX applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI. Architect and code conversational flows that are not just functional, but optimized for the "connective tissue" between Google's Conversational AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters. Build high-performance evaluation (Eval) pipelines and observability frameworks to optimize agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking. Identify repeatable field patterns and technical "friction points" in Google's AAI stack, converting them into reusable modules or product feature requests for engineering teams. Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
Sprachkenntnisse
- English
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