Senior Software Engineer, Backend/Distributed Systems - AI Security & Governance

Omnissa•Atlanta, GA

18h•Hybrid

About The Position

Omnissa is hiring a Senior Software Engineer to build Elara, a new product line that helps enterprises discover, monitor, and govern AI use across their devices. The role focuses on building scalable backend services and distributed systems for enterprise endpoint management, AI security monitoring, and governance workflows. Elara’s product includes a lightweight, native, always-on macOS agent that provides visibility into AI applications, network traffic, and the handling of sensitive data. The engineer will design and maintain Go-based cloud-native services and APIs, support secure communication with large numbers of endpoint agents, and develop storage and analytics capabilities for high-volume telemetry. The position also involves reliability, performance, observability, testing, and production operations, in collaboration with endpoint engineering, security, frontend, product, SRE, and platform teams. The Elara team uses AI coding agents, including Claude Code, Cursor, and GitHub Copilot, in design, implementation, testing, and debugging. Engineers remain responsible for architecture, security decisions, reviewing code, and the quality of shipped work. Documentation, CI, test automation, and reusable agent skills and prompts are treated as important parts of the development process. This is a hybrid role based in Atlanta, Georgia, or Mountain View, California, with no travel expected. A bachelor’s degree is preferred, or equivalent education and relevant professional experience. The position is not eligible for employment-based immigration sponsorship by Omnissa.

Requirements

At least 5 years of professional software engineering experience, including substantial experience building backend services and distributed systems.
Strong hands-on Go programming experience, including concurrency, goroutines, channels, context management, and performance profiling.
Solid understanding of distributed systems, microservices architecture, asynchronous processing, and fault-tolerant system design.
Strong PostgreSQL experience, including schema design, query optimization, indexing, transactions, and database performance tuning.
Hands-on experience with Redis or similar in-memory data stores for caching, high-throughput data access, and distributed application patterns.
Experience designing and developing RESTful APIs, gRPC services, and secure client-server communication.
Strong knowledge of data structures, algorithms, networking fundamentals, and concurrent programming.
Experience building scalable, highly available services in containerized environments using Docker and Kubernetes or equivalent orchestration platforms.
Familiarity with event-driven architectures, message queues, or streaming platforms such as Kafka.
Experience with observability, debugging, performance optimization, and production incident troubleshooting.
Understanding of authentication, authorization, encryption, and secure backend development practices.

Nice To Haves

Experience building large-scale SaaS platforms, endpoint management systems, cybersecurity products, or high-volume telemetry processing services.
Hands-on experience with ClickHouse, StarRocks, or similar real-time analytical databases.
Experience designing high-throughput ingestion pipelines, columnar storage architectures, and near-real-time analytics systems.
Knowledge of Apache Kafka, event-stream processing, and distributed messaging patterns.
Experience managing persistent connections with large numbers of distributed clients using gRPC streaming or similar protocols.
Experience with Redis Cluster, PostgreSQL partitioning, replication, connection pooling, and database scalability patterns.
Familiarity with Prometheus, Grafana, OpenTelemetry, Loki, or similar observability technologies.
Experience with Kubernetes, HashiStack (Nomad, Consul, Vault), or other cloud-native infrastructure platforms.
Knowledge of multi-tenant SaaS architectures, tenant-level data isolation, and enterprise security requirements.
Bachelor’s degree, or equivalent combination of education and relevant professional experience.

Responsibilities

Design, develop, and maintain scalable, high-performance backend services and APIs using Go.
Build cloud-native distributed microservices for enterprise endpoint management, AI security monitoring, and governance workflows.
Design secure, efficient communication between endpoint agents and backend services using gRPC, Protocol Buffers, HTTP/2, and REST APIs.
Optimize concurrency, resource utilization, throughput, and latency across distributed services.
Design secure multi-tenant architectures with strong tenant isolation, authorization, and data protection.
Develop automated unit, integration, performance, and end-to-end tests.
Participate in architecture reviews, code reviews, and technical design discussions.
Contribute to CI/CD automation, infrastructure as code, and reliable production deployments.
Collaborate with endpoint engineering, security, frontend, and product teams to deliver end-to-end capabilities.
Design and optimize transactional data models, queries, indexing strategies, and database interactions using PostgreSQL.
Implement high-performance caching, distributed coordination, and low-latency data access using Redis or Valkey.
Evaluate and integrate analytical data platforms such as ClickHouse, StarRocks, or Apache Druid for high-volume, low-latency analytical queries.
Design services for high availability, fault tolerance, horizontal scalability, and graceful degradation.
Optimize systems for high-concurrency workloads, large-scale telemetry ingestion, and low-latency policy operations.
Implement retry mechanisms, backpressure, rate limiting, idempotency, and failure recovery across distributed services.
Establish observability through metrics, structured logging, distributed tracing, and actionable alerting.
Conduct performance profiling, load testing, capacity planning, and production troubleshooting.
Collaborate with SRE and platform engineering teams to improve reliability, operational efficiency, and deployment automation.
Use AI coding agents as part of design, development, testing, and debugging while owning architecture, security judgment, and final code review.