We are Omnissa! 

Omnissa is the first AI‑driven digital work platform, built to support flexible, secure, work‑from‑anywhere experiences. We integrate industry-leading solutions—including Unified Endpoint Management, Virtual Apps and Desktops, Digital Employee Experience, and Security & Compliance—into a seamless, autonomous workspace that adapts to how people work. Our platform boosts employee engagement while optimizing IT operations, security, and cost. 

Guided by our Core Values—Act in Alignment, Build Trust, Foster Inclusiveness, Drive Efficiency, and Maximize Customer Value—we’re growing rapidly and committed to delivering meaningful impact. If you're passionate about shaping the future of work, we’d love to hear from you. 

What is the opportunity? 

The Omnissa AI Platform enables secure, enterprise‑grade LLM capabilities, RAG pipelines, agentic AI workflows, and advanced analytics across our SaaS products. We power copilots, autonomous task workflows, decision‑support systems, and intelligent automation—all built with a deep focus on security, trust, compliance, and scale. 

We are seeking a Manager or Sr Manager, Engineering (AI Platform: LLMs, Agentic AI, Data Science & Software Engineering) to lead a team building foundational services for: 

• LLM inference & orchestration 

• RAG pipeline architecture 

• Agentic AI frameworks (tool‑using AI agents, workflow orchestration, policy-governed autonomy) 

• Model evaluation, reliability & safety 

• Data & embeddings pipelines 

• AI observability and governance 

You will manage a team of high-performing  engineers and drive delivery across high‑impact SaaS platform capabilities used throughout Omnissa’s product ecosystem. 

What you’ll do 

AI Platform & Agentic Systems 

• Build a scalable SaaS AI platform for LLM inference, multimodal models, and agentic AI orchestration. 

• Design frameworks enabling tool‑calling agents, workflow sequencing, secure action policies, and human‑in‑the‑loop governance. 

• Own infrastructure for prompt management, evaluation, caching, latency optimization, and cost efficiency. 

Retrieval-Augmented Generation (RAG) & Data 

• Develop secure pipelines for document ingestion, embeddings, vector indexing, metadata policies, and relevance feedback. 

• Ensure compliance and data isolation for enterprise RAG at scale.

Evaluation, Safety & Observability 

• Build systems for LLM/agent evaluation, red‑teaming, safety checks, behavioural constraints, and continuous monitoring. 

• Deliver observability for latency, cost, drift, quality, and user feedback loops. 

SaaS Reliability & Platform Excellence 

• Drive uptime, scalability, multi‑tenant isolation, and efficient resource utilization across global SaaS workloads. 

• Establish CI/CD, automation, SLOs, runbooks, and production readiness for AI services. 

Leadership, People & Execution 

• Manage, mentor, and grow a team of engineers across AI, backend, platform, and systems domains. 

• Lead sprint planning, architectural reviews, backlog prioritization, and cross‑org collaboration. 

• Partner with product, research, UX, and security teams to define roadmap and execute against delivery targets. 

• Handle customer issues and escalations in your ownership areas. 

What you’ll bring to Omnissa 

Required 

• 12+ years industry experience, including 4+ years leading engineering teams. 

• Strong experience architecting and building enterprise-grade SaaS systems. 

• 8+ years hands‑on software development in Python and knowledge of Java, dotnet, Go 

• Deep experience in distributed systems, microservices, concurrency, and performance optimization. 

• Strong background with cloud platforms (AWS/Azure/GCP), Kubernetes, Amazon ECS  and containers 

• Experience with at least one area of the modern AI ecosystem:  

• LLM serving frameworks 

• RAG 

• MLOps 

• Observability 

• Strong understanding of data governance, compliance, access control, privacy, and secure data handling. 

• Bachelor’s in Computer Science or related field

Preferred (Nice to have) 

• Experience with agentic AI systems, including tool‑calling, orchestration, decision loops, or action‑policy frameworks. 

• Experience with fine‑tuning, LoRA, distillation, quantization . 

• Experience integrating AI with enterprise identity, workflow, or security ecosystems. 

• MS/PhD