Salvo Software

Security Engineer

• Design and maintain security architecture across cloud (AWS, GCP, Azure), on-prem, and endpoint environments using Zero Trust principles
• Define and enforce identity, access, and least-privilege policies across Active Directory, cloud IAM, VPNs, and internal tools
• Manage strict project-based access to sensitive, licensed OEM data
• Assess and harden cloud configurations, manage secrets, and monitor for misconfigurations and policy drift
• Integrate security into CI/CD pipelines, including code and dependency scanning, container image scanning, secrets detection, and secure pipeline design
• Define and review firewall, router, switch, and VPN configurations
• Set endpoint protection standards covering EDR, patching, disk encryption, and MFA
• Run vulnerability scans and penetration tests, prioritize findings, and drive remediation
• Implement and manage logging, SIEM, and alerting to detect and investigate suspicious activity
• Own incident response plans and lead investigation, containment, and post-incident analysis
• Lead alignment with SOC 2, ISO 27001, and similar security and compliance frameworks
• Support client security assessments and questionnaires
• Assess and secure AI/ML and LLM-based systems, including data access, model serving, and integrations
• Write and maintain security policies and run security awareness initiatives
• Evaluate the security of third-party tools and providers before adoption
• Collaborate closely with the DevOps Engineer and Technical Support Specialist, setting security standards and providing technical guidance across the company

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Senior Cybersecurity Engineer

IT Support

• Provide remote Tier 1 and Tier 2 support for users across multiple locations in the Americas, resolving hardware, software, network, and access issues
• Handle the full lifecycle of NUC computers and other devices, including provisioning, configuration, updates, troubleshooting, and retirement
• Manage user accounts, groups, and permissions in Active Directory and related systems
• Set up and remove access, devices, and accounts for incoming and departing team members
• Support VPN access and diagnose connectivity issues for remote users and office locations
• Support hardware upgrades and configurations, including routers, firewalls, and switches, in line with infrastructure standards
• Apply patches, maintain endpoint protection, and enforce MFA and other security policies
• Maintain accurate records of hardware, software licenses, and device assignments
• Maintain tickets, runbooks, and troubleshooting guides
• Escalate issues requiring deeper investigation to the DevOps Engineer with complete context

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Technical Support Specialist

Engineer

• Design and own end-to-end RAG architectures from document ingestion to grounded, cited responses
• Build advanced retrieval strategies including hybrid search, reranking, query rewriting/expansion, metadata filtering, and multi-step or agentic retrieval
• Define chunking and indexing strategies for structured and semi-structured sources, including XML/XSD, Office formats, PDFs, and scanned documents
• Select, benchmark, and fine-tune embedding models
• Manage vector databases such as pgvector, Qdrant, Weaviate, Milvus, and Azure AI Search at scale
• Build continuous evaluation frameworks for retrieval and generation quality
• Reduce hallucinations through grounding, citation, guardrails, and confidence handling
• Implement permission-aware and multi-tenant retrieval
• Develop MCP integrations exposing RAG systems and internal data sources to LLMs and agents
• Optimize LLM serving with Ollama and llama.cpp, including GGUF quantization, GPU/CUDA tuning, batching, caching, and offline/air-gapped deployments
• Set RAG architecture standards, reference implementations, and best practices
• Review designs and code and provide technical guidance to AI Developers
• Translate client and product requirements into retrieval strategies with quality, latency, and cost targets
• Evaluate emerging techniques such as GraphRAG, long-context trade-offs, and agentic RAG
• Deploy and scale RAG services on Microsoft Azure and AWS using Docker and Kubernetes
• Maintain CI/CD pipelines for AI services
• Build observability for tracing, retrieval diagnostics, latency, cost, and quality dashboards
• Support event-driven ingestion and re-indexing pipelines using Apache Kafka
• Troubleshoot root causes across the stack

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Expert RAG Engineer

Machine Learning Engineer

• Design, deploy, and operate MLOps platform infrastructure, cloud services (AWS, Azure, GCP), and LLM inference pipelines (Ollama, vLLM).
• Build automated CI/CD pipelines, Infrastructure as Code, and monitoring/alerting systems for RAG applications, AI agents, and production deployments.
• Manage Linux servers, networking, security endpoints, and troubleshoot production issues with AI engineering teams.
• Establish department-wide MLOps/DevOps standards, best practices, and reusable infrastructure templates.
• Guide AI Developers and RAG Engineers on architecture, deployment strategies, tool selection, and production readiness.
• Translate feature requirements into reliable, secure, cost-effective infrastructure and maintain comprehensive system documentation.
• Provision containerized workloads, Linux servers, and infrastructure using Terraform and Ansible across cloud and on-prem environments.
• Manage CI/CD with GitHub Actions and Azure DevOps, observability with Prometheus and Grafana, secure networking, and GPU-enabled hardware.
• Troubleshoot platform incidents, perform root cause analysis, and maintain operational resilience.

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Senior MLOps Engineer

DevOps Engineer

• Design, manage, and optimize AWS, GCP, or Azure cloud infrastructure for internal needs and client-facing digital transformations
• Define and manage infrastructure as code using Terraform or similar tools
• Build, maintain, and troubleshoot CI/CD pipelines
• Oversee Docker and Kubernetes environments for scalable, reliable application hosting
• Manage monitoring, logging, and alerting systems
• Define and enforce identity, access management, and least-privilege controls
• Manage strict access levels for sensitive, licensed OEM data to support project-based compliance
• Monitor cloud spend and right-size resources
• Set up, integrate, and maintain AI/ML systems and tooling
• Develop and maintain automation scripts and internal tooling using Python, Bash, or similar languages
• Guide the infrastructure roadmap and evaluate and recommend tools and practices

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AI Engineer

• Serve and operate open-source LLMs using Ollama and llama.cpp locally and in on-premises environments
• Build and maintain RAG pipelines involving embeddings, vector databases, chunking strategies, and retrieval quality
• Develop MCP integrations connecting LLMs to internal tools and data sources
• Build Python backend services for ML inference and AI-driven product features
• Parse and process structured and semi-structured data, including XML/XSD and Office document formats
• Grow into model optimization, including GGUF quantization, GPU/CUDA tuning, and offline/air-gapped deployments
• Build and maintain CI/CD pipelines for AI services
• Deploy AI workloads to Microsoft Azure and AWS and containerize services with Docker
• Automate operational tasks with Python, Bash, and/or PowerShell
• Troubleshoot across the stack and identify root causes
• Support event-driven architectures using Apache Kafka producers and consumers
• Work with engineering and product teams to take LLM-powered features from prototype to reliably deployed systems

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Open roles

Senior Cybersecurity Engineer

Technical Support Specialist

Expert RAG Engineer

Senior MLOps Engineer

DevOps Engineer

AI Developer