Lead Applied AI / GenAI Engineer

Qualys•Foster City, CA

19h

About The Position

We’re looking for an experienced engineer to design and deliver AI-powered applications, intelligent workflows, and automation solutions that drive measurable business impact. In this role, you’ll operate at the intersection of enterprise application engineering, automation, data integration, and AI systems deployment. You’ll apply strong systems thinking to connect model APIs, data sources, and workflow layers—unlocking improvements in speed, quality, and outcomes. Focused on applied AI and GenAI, you will bridge emerging capabilities with real-world enterprise use cases, building and scaling production-grade AI solutions in a highly collaborative environment.

Requirements

7–12+ years of experience in software engineering, applied AI, or ML engineering
Hands-on experience building and deploying AI/ML or GenAI applications in production, retrieval-augmented generation, workflow orchestration, API development, system integration, agent or tool calling, prompt design, evaluation frameworks, or AI observability
Strong programming skills (Python + backend engineering)
Experience integrating applications, data sources, or internal platforms through APIs, services, event-driven patterns, or workflow tools.
Strong systems thinking and the ability to work across data, model, application, and infrastructure layers.
Ability to operate with incomplete information, break down ambiguous problems, and move from concept to implementation with appropriate judgment.

Nice To Haves

Experience with RAG architectures, prompt engineering, and LLM evaluation
Familiarity with data engineering concepts (SQL, pipelines)
Exposure to vector databases, embeddings, semantic search
Experience integrating AI into enterprise workflows/applications
Background in automation, intelligent agents, or workflow orchestration

Responsibilities

Build and deploy AI-enabled RAG pipelines and intelligent workflows that connect enterprise systems, data sources, model APIs, and automation layers using modern ML and LLMs
Design and evolve backend services, APIs, and integrations, including retrieval, prompting, tool-calling, and orchestration patterns for use cases such as knowledge assistants, workflow automation, and decision support
Leverage AI-assisted development to accelerate delivery, applying a strong “editor” mindset to rigorously review, secure, and standardize AI-generated code for reliability, security, observability, and documentation
Develop and implement evaluation frameworks to measure output quality, retrieval accuracy, latency, and failure modes, and continuously improve system performance
Lead structured problem formulation with cross-functional stakeholders (IT, Security, Data, HR, Finance, and business teams) to ensure AI is applied to high-impact, real-world problems
Rapidly prototype solutions and scale them to production-grade deployments with appropriate guardrails, monitoring, and governance
Optimize AI systems for performance, cost, scalability, and reliability in enterprise environments
Collaborate with platform and data teams to effectively leverage enterprise data assets
Build and contribute reusable components, frameworks, and playbooks to accelerate development and reduce duplication across the engineering ecosystem