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Job Description
Design, build, and scale enterprise-grade AI/GenAI solutions that leverage enterprise data platforms and business applications to automate processes, enhance decision-making and deliver measurable business outcomes. This role focuses on defining and implementing the foundational AI strategy for the CIO organization by combining AI technologies, modern data platforms, enterprise systems, APIs, and analytics in a secure, scalable, and production-ready environment. The ideal candidate brings experience building AI solutions on platforms such as Data Lake or Lakehouse architectures and can bridge business, data and technology teams to drive enterprise-wide AI adoption and innovation.
Responsibilities
- Define and help execute the Enterprise AI strategy and roadmap across enterprise applications, data platforms and enterprise workflows.
- Design, develop and deploy AI/GenAI applications, RAG solutions, intelligent agents and workflow automation leveraging enterprise data and modern AI technologies.
- Leverage modern Data Lake, Lakehouse and cloud data platforms to design, implement and scale AI solutions using enterprise data.
- Build AI capabilities on top of enterprise data platforms, including structured, unstructured, and real-time data sources, enabling scalable and governed AI adoption.
- Architect backend services, APIs, integrations, and orchestration frameworks using LLMs, model APIs, enterprise systems and governed data.
- Define AI governance, evaluation, observability and performance frameworks to ensure quality, security, reliability, and business value.
- Collaborate with Business and Enterprise Application teams to identify and deliver high-impact AI use cases.
- Rapidly prototype and scale AI solutions into secure, production-ready deployments while optimizing performance, scalability, and cost.
- Develop reusable AI frameworks, architecture patterns, and best practices that accelerate enterprise-wide AI innovation and adoption.
Required Qualifications
- 7–12+ years of experience in Software Engineering, AI/ML, or GenAI application development.
- Proven experience designing and implementing AI/ML or GenAI solutions on modern cloud-based data platforms such as Snowflake, Databricks, OCI, Lakehouse, data lake or enterprise analytics ecosystems.
- Strong understanding of enterprise Data Architectures, Data Lakes, Lakehouse, semantic layers, data pipelines, governance, and AI-ready data foundations.
- Strong expertise in Python, backend development, APIs, system integration, and workflow orchestration.
- Hands-on experience with RAG, LLMs, prompt engineering, tool/agent calling, AI evaluation, and observability.
- Experience integrating enterprise applications, data platforms, and services using APIs and event-driven architectures.
- Strong analytical, systems thinking, and problem-solving skills with the ability to translate ambiguous business problems into scalable AI solutions.
Preferred Qualifications
- Experience with vector databases, embeddings, semantic search, and advanced RAG architectures.
- Experience designing and implementing AI/GenAI solutions leveraging enterprise Data Lakes, Lakehouse, and large-scale analytical data platforms.
- Understanding of data governance, semantic layers, data quality, lineage, security, and compliance considerations required for enterprise AI implementations.
- Knowledge of SQL, data engineering, and data pipelines.
- Experience integrating AI into enterprise applications and business workflows.
- Exposure to intelligent agents, automation platforms, and AI governance frameworks.
- Experience building reusable AI frameworks, accelerators, or enterprise AI platforms.
Educational Qualification
BE/B.Tech/MCA, preferably in Computer Science, Information Technology, or a related field.
Skills Required
- 7-12+ years experience in Software Engineering, AI/ML, or GenAI application development.
- Proven experience building and deploying production-grade AI/GenAI solutions.
- Strong expertise in Python, backend development, APIs, system integration, and workflow orchestration.
- Hands-on experience with RAG, LLMs, prompt engineering, tool/agent calling, AI evaluation, and observability.
- Experience integrating enterprise applications, data platforms, and services using APIs and event-driven architectures.
- Strong analytical, systems thinking, and problem-solving skills; translate ambiguous business problems into scalable AI solutions.
- Experience with vector databases, embeddings, semantic search, and advanced RAG architectures.
- Knowledge of SQL, data engineering, and data pipelines.
- Experience integrating AI into enterprise applications and business workflows.
- Exposure to intelligent agents, automation platforms, and AI governance frameworks.
- Experience building reusable AI frameworks, accelerators, or enterprise AI platforms.
Qualys Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Qualys and has not been reviewed or approved by Qualys.
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Affordable Benefits — Benefits costs are widely viewed as low for employees and dependents, with healthcare often described as almost fully paid for. Feedback suggests this affordability helps offset perceptions of lower base pay in some roles.
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Healthcare Strength — Healthcare offerings are broad, including multiple medical plan options, dental and vision coverage, mental health support, and disability insurance. Benefits are described as “pretty amazing” or “great,” reinforcing perceived quality and coverage depth.
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Equity Value & Accessibility — Equity participation is accessible through company stock plans and an employee stock purchase plan. Compensation packages commonly include equity alongside salary and bonus, which some consider a meaningful part of total rewards.
Qualys Insights
What We Do
Qualys, Inc. (NASDAQ: QLYS) is a pioneer and leading provider of disruptive cloud-based security, compliance and IT solutions with more than 10,000 subscription customers worldwide, including a majority of the Forbes Global 100 and Fortune 100. Qualys helps organizations streamline and automate their security and compliance solutions onto a single platform for greater agility, better business outcomes, and substantial cost savings. The Qualys Cloud Platform leverages a single agent to continuously deliver critical security intelligence while enabling enterprises to automate the full spectrum of vulnerability detection, compliance, and protection for IT systems, workloads and web applications across on premises, endpoints, servers, public and private clouds, containers, and mobile devices. Founded in 1999 as one of the first SaaS security companies, Qualys has strategic partnerships and seamlessly integrates its vulnerability management capabilities into security offerings from cloud service providers, including Amazon Web Services, the Google Cloud Platform and Microsoft Azure, along with a number of leading managed service providers and global consulting organizations. For more information, please visit http://www.qualys.com
