Bizoforce Hiring – FDE Gen AI | San Jose, California, USA

Bizoforce Hiring is seeking an experienced Forward Deployed Engineer (FDE – Generative AI) to build, integrate, and deploy production-grade AI applications within enterprise customer environments. This role requires a hands-on engineering professional who can bridge the gap between advanced AI capabilities and real-world business requirements by developing, debugging, and delivering scalable agentic AI solutions. The ideal candidate will have strong expertise in Python, Google Cloud Platform (GCP), Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Google's Conversational AI ecosystem. The role involves close collaboration with customer engineering teams, solving complex integration and data-readiness challenges, and transforming prototypes into reliable, secure, production-ready AI systems.

Experience (in Years)

• Required Experience: 8–15 years of overall professional experience.
• Software Development: At least 5 years of experience developing software using Python or similar programming languages.
• AI Engineering: Demonstrated experience designing and deploying production-grade AI/ML solutions on cloud platforms.
• Customer-Facing Engineering: Experience leading technical discovery sessions and delivering AI solutions in customer environments.

Required Skills

• Programming: Strong hands-on expertise in Python or equivalent programming languages.
• Generative AI and LLMs: Experience building enterprise-grade applications powered by Large Language Models.
• Google Cloud Platform (GCP): Experience architecting, developing, and deploying AI systems on GCP.
• Google Conversational AI Ecosystem: Hands-on implementation and customization of:

o Conversational Agents and Gemini-powered Customer Experience (CX) o Customer Engagement Suite (CES) o Contact Center AI (CCAI)

• RAG and Vector Databases: Experience building pipelines for structured and unstructured data using vector databases and Retrieval-Augmented Generation architectures.
• Agentic AI: Experience designing and developing production-grade AI agents and workflows.
• API and Enterprise Integration: Ability to integrate AI products with customer APIs, legacy systems, enterprise data sources, and security infrastructure.
• Production Deployment: Experience taking AI-driven solutions from conception through development, testing, deployment, and production launch.
• Customer Collaboration: Ability to conduct technical discovery, understand business requirements, and work directly with customer engineering teams.
• Evaluation and Observability: Experience developing evaluation pipelines and monitoring frameworks to measure AI accuracy, safety, performance, and latency.

Preferred Skills / Good-to-Have Technical Experience

• Advanced Education: Master's degree or PhD in Artificial Intelligence, Computer Science, Engineering, or a related technical field.
• Multi-Agent Frameworks: Experience with LangGraph, CrewAI, Agent Development Kit (ADK), or similar frameworks.
• Advanced Agentic Patterns: Familiarity with ReAct, self-reflection, hierarchical delegation, and multi-agent orchestration.
• Model Context Protocol (MCP): Experience building MCP servers and integrating tools and services into agentic workflows.
• LLM Performance Optimization: Understanding of tokens per second, cost per request, response latency, and other LLM-specific performance metrics.
• State Management and Tracing: Experience optimizing conversational state, managing agent context, and implementing granular tracing.
• Google Enterprise CX and Voice AI: Production-grade experience with:

o Dialogflow CX o CX Agent Studio o SCRAPI o Agent Assist o Contact Center as a Service (CCaaS)

• Telecom Architecture: Knowledge of telecom APIs, user authentication (UA), and enterprise business architecture (BAA).
• Technical Assessment and Certification: Google AI/FDE Bootcamp certification or equivalent verified engineering assessments demonstrating strong technical proficiency.
• Technical Leadership: Experience working independently as a senior engineer, driving complex implementations, and mentoring or upskilling partner engineering teams.

Qualifications

• Bachelor's degree in Engineering, Computer Science, a related technical discipline, or equivalent practical experience.
• Strong software engineering fundamentals and the ability to develop maintainable, production-ready code.
• Demonstrated experience architecting cloud-based AI systems and delivering enterprise AI solutions.
• Practical understanding of data pipelines, vector search, RAG architectures, and LLM-based applications.
• Hands-on experience implementing and customizing Google's Conversational AI products.
• Ability to translate customer requirements into technical designs and working solutions.
• Strong analytical, debugging, communication, and problem-solving skills.
• Ability to work autonomously while collaborating effectively with customer stakeholders, engineering teams, and delivery partners.

Key Responsibilities

1. Generative AI Application Development

• Design, develop, and deploy complex Generative AI applications for enterprise customers.
• Transform rapid prototypes into reliable, scalable, production-grade agentic workflows.
• Build multi-agent systems, MCP servers, and tool-integrated AI applications that deliver measurable business value.
• Write clean, maintainable, and efficient code while ensuring production readiness.

2. Google Cloud and Enterprise Integration

• Architect and implement integrations between Google's AI products and customer infrastructure.
• Connect Gemini-powered CX, Customer Engagement Suite, and Contact Center AI with customer APIs, legacy systems, enterprise data sources, and security environments.
• Identify and resolve integration challenges, data-readiness gaps, and system compatibility issues.
• Ensure AI solutions operate reliably within enterprise security and infrastructure requirements.

3. Conversational AI and Contact Center Solutions

• Implement and customize Google's Conversational AI ecosystem for customer engagement and contact center use cases.
• Develop and optimize conversational agents and voice-based AI experiences.
• Integrate Dialogflow CX, CX Agent Studio, Agent Assist, CCAI, and related enterprise CX technologies as applicable.
• Improve conversational workflows, agent performance, and end-user experience through iterative development and testing.

4. RAG, Data Pipelines, and Knowledge Integration

• Develop data pipelines for structured and unstructured enterprise data.
• Implement vector database integrations and RAG architectures to support accurate, context-aware AI responses.
• Address data quality, retrieval relevance, and information accessibility challenges.
• Optimize data preparation, retrieval, and context management for enterprise AI applications.

5. AI Evaluation, Safety, and Observability

• Build automated evaluation pipelines to assess AI system accuracy, reliability, and safety.
• Implement observability frameworks to monitor latency, performance, and operational behavior.
• Track LLM-specific metrics, including token consumption, throughput, and cost per request.
• Optimize state management, tracing, and debugging to improve system efficiency and reliability.

6. Customer Engagement and Technical Discovery

• Lead technical discovery sessions with customers to understand business objectives, existing systems, and deployment requirements.
• Translate customer needs into practical technical architectures and implementation plans.
• Work directly with customer engineering teams to co-develop and deploy AI solutions.
• Provide hands-on engineering support throughout implementation, testing, troubleshooting, and production rollout.

7. Product Feedback and Continuous Improvement

• Identify recurring implementation challenges and patterns across customer deployments.
• Convert field insights into reusable components, engineering recommendations, and formal product feature requests.
• Collaborate with product and engineering teams to improve Google's AI product capabilities.
• Apply lessons learned from deployments to strengthen future implementations and accelerate delivery.

8. Technical Leadership and Knowledge Sharing

• Independently drive complex technical initiatives and resolve critical engineering blockers.
• Establish and promote software engineering, testing, deployment, and observability best practices.
• Mentor customer and partner engineering teams to improve their technical capabilities.
• Support long-term solution sustainability, operational readiness, and end-user adoption.

Expected Deliverables

• Production-ready Generative AI applications and agentic workflows.
• Integrated Google Cloud and Conversational AI solutions connected to enterprise systems.
• Functional RAG pipelines and vector database integrations.
• Automated AI evaluation and observability frameworks.
• Optimized AI solutions meeting performance, safety, accuracy, and latency requirements.
• Reusable technical components and documented recommendations for product improvements.
• Technical documentation, implementation guidance, and knowledge transfer for customer engineering teams.

Assignment Details

• Job Title: FDE Gen AI
• Job Location: San Jose, California, USA
• Work Mode: On-site — customer location attendance required from Day 1
• Job Type: Full Time
• Open Positions: Multiple
• Working Hours: 9:00 AM – 5:00 PM
• Hours per Week: 40 hours
• Timesheet Cycle: Weekly
• Job Status: Active