Job Description:

We are Omnissa. The world is evolving quickly, and organizations everywhere—from global enterprises to educational institutions—are under pressure to deliver flexible, work-from-anywhere experiences. They need secure, scalable, seamless digital work environments that empower employees and customers to access applications from any device, on any cloud. That’s where Omnissa comes in. 

The Omnissa Platform is the first AI driven digital work platform designed to deliver smart, seamless, and secure work experiences from anywhere. We uniquely integrate industry leading solutions in Unified Endpoint Management, Virtual Apps and Desktops, Digital Employee Experience, and Security & Compliance—all unified through shared data, identity, administration, and automation services. Built on the vision of autonomous workspaces—self configuring, self-healing, and self-securing—Omnissa continuously adapts to how people work, optimizing user experience, IT efficiency, security posture, and cost. 

As a global private company with over 4,000 employees, we’re growing rapidly. If you're passionate about building AI systems that operate at a massive scale and shape the future of work, we’d love to meet you. 

What is the opportunity?

Our platform manages millions of devices across multiple operating systems, requiring exceptional performance, scalability, availability, and resilience. You will join the AI Platform Team, responsible for building and enabling AI-driven application experiences across the Omnissa product ecosystem.

As an AI Application Software Engineer, you will design, develop, and integrate intelligent application features powered by AI/ML technologies, including LLMs and data-driven components. You will work closely with platform, product, and engineering teams to deliver seamless AI-powered capabilities within a cloud-scale environment while adhering to strong software engineering practices.

You will own engineering initiatives end-to-end and help foster a culture of high ownership, continuous improvement, and engineering excellence.

Responsibilities

• Design, develop, and deliver AI-powered application features and services.

• Build and integrate AI capabilities such as LLM-based features, automation workflows, and intelligent user experiences into applications.

• Develop backend services, APIs, and application logic that interact with AI/ML systems and models.

• Collaborate with AI/ML engineers to integrate models into production-grade applications.

• Build scalable and reliable distributed systems, ensuring performance and high availability.

• Implement observability, monitoring, and logging for AI-driven application components.

• Participate in system design, architecture discussions, and code reviews.

• Continuously improve system performance, reliability, and developer productivity.

• Stay current with advancements in AI technologies (e.g., LLMs, embeddings, agent frameworks) and apply them to product use cases.

What will you bring to Omnissa?

Required Skills & Experience

• 8 to 16 years of experience as a Software Engineer role in building scalable applications.

• Strong proficiency in Python and at least one additional programming language (Java, Go, or C++).

• Experience developing backend systems, APIs, and microservices architectures.

• Experience building scalable services using REST/gRPC APIs.

• Experience integrating AI/ML capabilities into applications (e.g., APIs for LLMs or ML services).

• Strong understanding of data structures, algorithms, and system design principles.

• Experience with containerization and orchestration technologies

• Strong problem-solving skills and ability to collaborate effectively in Agile environments.

• Highly motivated, adaptable, and eager to learn new technologies.

Preferred Skills

• Experience building applications using LLMs, RAG systems, or AI agents.

• Familiarity with vector databases and embedding models.

• Experience with orchestration frameworks (e.g., LangChain, LangGraph).

• Knowledge of real-time data processing and event-driven architectures.

• Exposure to observability tools and monitoring systems for AI applications.