Lead Software Engineer

Strong expertise in Java, APIs, Angular, and React

Experience building Agentic Frameworks utilizing MCP layers and LLMs

Strong logical design, analytical, and solution architecture skills based on product and platform requirements

BS/BA in Computer Science, Information Systems, or related field

Strong full-stack development background with:

Java

API Development

Angular

React

Experience with Agentic AI frameworks, MCP architecture, and LLM integrations

Experience with Qlik Sense or similar BI platforms (Tableau, Power BI, WebFocus)

Strong understanding of data architecture and high-performance data concepts:

File stores

Wide-column databases

Key-value databases

Excellent communication, analytical, and problem-solving skills

Ability to work independently in a fast-paced environment

Hadoop experience

Kafka experience

Agile/Scrum methodology experience

Ideal Candidate: A senior-level engineer who combines strong full-stack Java development skills with hands-on experience building Agentic AI frameworks (MCP + LLMs), while also possessing the analytical and design skills needed to support enterprise data platforms and Qlik Sense environments.

Seeking a Lead Software Engineer with strong full-stack development experience and expertise in Agentic AI frameworks. This role will support the installation, configuration, administration, and enhancement of a Qlik Sense environment while helping develop next-generation Agentic products and data solutions.

Design and develop Agentic Frameworks supporting AI-driven products

Build and maintain internal and external dashboards using Qlik Sense

Partner with cross-functional teams to gather functional and system requirements

Install, configure, and administer Qlik Sense applications

Expand and manage Qlik clusters as needed

Manage dashboard deployments, user access, security rules, and data sources

Troubleshoot dashboard and platform issues

Design, develop, and implement DataMarts with a focus on quality and data integrity

Provide technical guidance to Data Engineering teams

Create technical documentation, process diagrams, and flowcharts

Evaluate platform performance and cluster architecture with leadership teams

Interpret conceptual, logical, and physical data models