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Gen AI Forward Deployed Engineer

Zenith Infotek LlcEngineering & Development
Hiring from
United States
Work type
Remote
Posted
Oct 2, 2026
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GenAI Forward Deployed Engineer (FDE)
Location: Remote
Work Hours: PST
Employment Type: Contract / Full-Time

Job Title: GenAI Forward Deployed Engineer (FDE)
Location: Remote
Work Hours: PST
Duration: 8+ Months (Contract)
Interview: Video Interview

Job Summary
We are seeking a highly hands-on GenAI Forward Deployed Engineer (FDE) to build and deploy production-grade AI solutions for enterprise customers.
The FDE acts as an innovator-builder, bridging the gap between advanced Google Cloud AI products and real-world enterprise environments. This role goes beyond advisory architecture—you will design, code, integrate, debug, deploy, and optimize sophisticated agentic AI applications directly with customer engineering teams.
The ideal candidate is a high-agency engineer with strong software development skills, cloud architecture expertise, enterprise AI experience, and the ability to independently drive complex technical engagements from discovery through production.
Key Responsibilities
  • Build and deploy complex GenAI and agentic AI applications, moving solutions from prototypes to production.
  • Develop multi-agent workflows, MCP servers, RAG architectures, and enterprise AI applications that deliver measurable business value.
  • Architect and implement integrations between Google Cloud AI products and customer environments, including APIs, legacy systems, enterprise data sources, and security boundaries.
  • Work with technologies across the Google Enterprise CX ecosystem, including Gemini, Conversational Agents, Customer Engagement Suite (CES), and Contact Center AI (CCAI).
  • Build data pipelines for structured and unstructured enterprise data, including vector databases and RAG-based architectures.
  • Develop evaluation frameworks and observability solutions to measure accuracy, safety, latency, cost, and overall agent performance.
  • Troubleshoot production blockers involving data readiness, integrations, authentication, state management, security, and system performance.
  • Lead technical discovery sessions with customer engineering and business stakeholders.
  • Collaborate directly with customer teams to design, implement, test, and productionize AI solutions.
  • Identify recurring implementation challenges and convert field learnings into reusable components, accelerators, or product feedback for engineering teams.
  • Establish engineering best practices and help customer teams successfully maintain and scale deployed solutions.
  • Independently drive execution on complex customer engagements while mentoring and upskilling partner engineering teams.
Minimum Qualifications
  • Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
  • 5+ years of professional software development experience, preferably with Python or similar programming languages.
  • Strong experience architecting and deploying AI/ML systems on cloud platforms, preferably GCP.
  • Hands-on experience building enterprise AI solutions using:
    • RAG architectures
    • Vector databases
    • Structured and unstructured data pipelines
    • LLM/GenAI applications
  • Proven experience taking production-grade AI solutions from concept through deployment and launch.
  • Experience conducting technical discovery sessions and working directly with enterprise customers.
  • Hands-on implementation and customization experience with Google's conversational AI ecosystem, including:
    • Dialogflow / Conversational Agents
    • Gemini-powered CX
    • Customer Engagement Suite (CES)
    • Contact Center AI (CCAI)
Preferred Qualifications
  • Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline.
  • Experience building multi-agent systems using frameworks such as:
    • LangGraph
    • CrewAI
    • Google ADK
  • Experience implementing advanced agentic patterns such as:
    • ReAct
    • Self-reflection
    • Hierarchical delegation
    • Multi-agent orchestration
  • Strong understanding of LLM-native performance metrics, including:
    • Tokens per second
    • Cost per request
    • Latency
    • Model utilization
    • Agent accuracy
  • Experience with state management, tracing, evaluation, and observability for agentic systems.
  • Experience with production-grade conversational and voice AI systems across:
    • Dialogflow CX
    • CX Agent Studio
    • Agent Assist
    • CCAI / CCaaS
    • SCRAPI
  • Strong understanding of enterprise authentication, APIs, security architecture, and integration patterns.
  • Experience working with telecommunications APIs and enterprise telecom architectures.
  • Ability to operate as a senior tiger-team engineer, independently solving ambiguous and technically complex problems.
  • Experience mentoring and enabling customer or partner engineering teams.

This is a remote position.





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