Gen AI Forward Deployed Engineer
ZENITH INFOTEK LLC
•Remote
About The Position
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.
Requirements
- 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).
Nice To Haves
- 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.
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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
