Bizoforce Hiring – Tech Lead – Software Engineer (GenAI Forward Deployed Engineer) | San Jose, California, USA

Bizoforce is hiring an experienced Tech Lead – Software Engineer specializing in Generative AI for a forward-deployed engineering role focused on building and deploying production-grade AI solutions. The ideal candidate will combine strong software engineering skills with expertise in cloud-based AI systems, agentic workflows, Retrieval-Augmented Generation (RAG), and enterprise data integration. This role involves working directly with customer engineering teams to transform AI prototypes into reliable, secure, and scalable production applications. The candidate will solve complex integration and data-readiness challenges, develop evaluation and observability frameworks, and help improve AI products through real-world implementation feedback.

Experience (in Years)

• Required Experience: 5–15 years
• Relevant Experience: At least 5 years of software development experience using Python or similar programming languages.
• Technical Leadership: Experience architecting cloud-based AI systems, delivering production-grade AI solutions, and leading technical discovery sessions with customers.

Required Skills

• Programming: Strong software development skills in Python or similar programming languages.
• Generative AI: Experience designing, developing, and deploying enterprise AI applications.
• Cloud Platforms: Experience architecting AI systems on cloud platforms such as Google Cloud Platform (GCP).
• Agentic AI: Ability to develop production-grade agentic workflows and AI-powered applications.
• RAG Architectures: Experience building Retrieval-Augmented Generation solutions for enterprise applications.
• Vector Databases: Experience working with vector databases and structured and unstructured data pipelines.
• API and System Integration: Ability to integrate AI applications with APIs, legacy systems, enterprise data sources, and security environments.
• Production AI Deployment: Experience taking AI-driven solutions from conception through development, deployment, and launch.
• Evaluation and Observability: Ability to build evaluation pipelines and monitoring frameworks for accuracy, safety, latency, and performance.
• Customer Engagement: Experience leading technical discovery sessions and collaborating with customer engineering teams.
• Problem-Solving: Strong analytical skills and the ability to resolve complex technical and integration challenges.

Preferred Skills / Good-to-Have Technical Experience

• Master's or PhD in Artificial Intelligence, Computer Science, or a related technical field.
• Experience implementing multi-agent systems using frameworks such as LangGraph, CrewAI, or Agent Development Kit (ADK).
• Knowledge of advanced agentic patterns, including ReAct, self-reflection, and hierarchical delegation.
• Understanding of LLM performance metrics, including tokens per second and cost per request.
• Experience optimizing AI state management, tracing, and observability.
• Familiarity with Model Context Protocol (MCP) servers and multi-agent architectures.
• Experience developing reusable AI modules and contributing technical feedback to product engineering teams.
• Knowledge of AI safety, application security, and production reliability best practices.

Qualifications

• Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• At least 5 years of software development experience using Python or similar languages.
• Demonstrated experience architecting and deploying AI solutions on cloud platforms.
• Hands-on experience with RAG, vector databases, and enterprise data pipelines.
• Proven ability to transition AI prototypes into production-ready applications.
• Experience collaborating with customers and cross-functional engineering teams.
• Strong communication, technical leadership, and stakeholder management skills.

Key Responsibilities

AI Application Development and Deployment

• Develop complex AI applications, progressing from rapid prototypes to production-grade agentic workflows.
• Build multi-agent systems and MCP-based solutions that address customer requirements and deliver measurable business value.
• Design, code, debug, and deploy bespoke AI applications within customer environments.
• Ensure AI solutions are scalable, secure, reliable, and suitable for enterprise use.

Cloud Architecture and Enterprise Integration

• Design and implement integrations between Google Cloud AI products and customer infrastructure.
• Connect AI applications with APIs, legacy systems, enterprise data sources, and existing security environments.
• Address integration complexity, data-readiness issues, and state-management challenges.
• Build structured and unstructured data pipelines to support enterprise AI and RAG applications.

Evaluation, Monitoring, and Optimization

• Develop high-performance evaluation pipelines and observability frameworks.
• Measure and improve AI system accuracy, safety, latency, and reliability.
• Optimize state management, tracing, throughput, and cost efficiency.
• Establish appropriate evaluation and monitoring practices for production-grade AI systems.

Customer Collaboration and Technical Leadership

• Work directly with customer engineering teams to identify requirements and resolve technical blockers.
• Lead technical discovery sessions and translate business needs into effective AI solutions.
• Collaborate with customer teams to establish engineering best practices and support long-term adoption.
• Provide hands-on technical leadership throughout solution development and deployment.

Innovation and Continuous Improvement

• Identify recurring implementation challenges and patterns across customer engagements.
• Convert field insights into reusable software modules, technical recommendations, and product feature requests.
• Collaborate with product and engineering teams to improve AI capabilities and deployment processes.
• Promote continuous improvement in enterprise AI development, delivery, and end-user adoption.

ssignment Details

• Job Location: San Jose, California, USA
• Work Mode: On-site
• 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