Job Description
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 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, the group responsible for building foundational AI capabilities across the Omnissa product ecosystem.
As a Senior Data Scientist, you will lead and innovate within the data science team to drive significant advancements in our AI/ML and data analytics capabilities. This is a hands-on role, and you will be expected to develop AI/ML-based solutions, work closely with cross-functional teams to solve complex business problems, and contribute to the company’s vision through advanced analytics, machine learning, and artificial intelligence.
You’ll work closely with engineering and product teams to operationalize models across our cloud-scale environment while driving best-in-class ML engineering practices.
Responsibilities
Lead the design and development of advanced data science and machine learning models using structured, unstructured, and semi-structured data.
Work with engineers to design and implement scalable machine learning pipelines, covering all stages from data ingestion and feature extraction to training, testing, validation, inference, and continuous learning in production systems.
Leverage key technologies and state-of-the-art tools necessary for data exploration, querying, visualization, and advanced analytics, including feature engineering, statistical analysis, and relationship discovery.
Be an expert in and lead the development of AI/ML solutions based on Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and AI Agents.
Optimize ML models and pipelines for performance, scalability, reliability, and cost efficiency.
Design and implement prompt engineering strategies to optimize the performance of LLM-based applications.
Collaborate with cross-functional teams to integrate ML solutions into core platform features and services.
Partner with engineering teams to deploy, monitor, and continuously improve AI/ML solutions in production environments.
Contribute to the establishment of best practices for model evaluation, experimentation, governance, and responsible AI.
Required Skills & Experience
9 to 14 years of experience in data science, machine learning engineering, or applied AI roles.
Hands-on experience with one or more of the following areas is required; experience across multiple areas is strongly preferred: supervised and unsupervised learning, classification and regression, clustering, time-series analysis, anomaly detection, recommendation systems, reinforcement learning, information retrieval, and natural language processing (NLP).
Highly proficient in Python and working knowledge of at least one other programming language such as Java or C++.
Extensive experience with AI/ML frameworks and libraries such as Scikit-learn, NumPy, Pandas, SciPy, and Hugging Face Transformers.
Hands-on experience with Large Language Models (LLMs), including fine-tuning, prompt engineering, evaluation, and deployment.
Knowledge of text embedding models and vector databases for Retrieval Augmented Generation (RAG) systems.
Experience with orchestration frameworks (e.g., LangChain, LangGraph) to build AI agents and multi-agent systems.
Familiarity with cloud platforms and tools such as AWS, Azure, or Google Cloud for development and deployment of scalable AI/ML models.
Experience with distributed computing frameworks (e.g., Spark, Ray).
Experience deploying and operating machine learning models in production environments.
Strong understanding of software engineering fundamentals, including testing, version control, CI/CD, and observability.