MLOps Engineer - ML Security Operations

The Senior Engineer will serve as a subject matter expert in Generative AI, cloud integrations, and intelligent automation. This role will design, build, and operationalize secure, scalable Generative AI solutions and autonomous AI agents while supporting the identification, monitoring, reporting, and reduction of security risks associated with Generative AI and Agentic AI. The role will also support the Security Champion practice by promoting security principles, engineering standards, and controls across the enterprise., Job Title: Senior Cloud Network Engineer Work Location & Reporting Address: Phoenix AZ 85054 (Hybrid) Vendor Rate: 65.00 Contract duration: 6 months Target Start Date: 28 Sep 2…

• 22 hours ago
• Apply easily, Job Description: Overview The Security Engineer II is a mid-level individual contributor responsible for implementing, administering, and maintaining the security controls that…
• 3 days ago +

Senior Engineer Generative AI, Cloud Integrations & Automation with strong technical expertise and demonstrated leadership capabilities to contribute to the success of enterprise-wide security initiatives., * Cloud Platforms: Strong experience with AWS, Azure, or Google Cloud, including compute, storage, networking, IAM, serverless technologies, and managed AI services.

• Generative AI / LLMs: Hands-on experience integrating Large Language Models (LLMs), such as OpenAI/Azure OpenAI, Anthropic Claude, Google Gemini, or open-source models, into enterprise applications.
• GenAI Architecture: Strong knowledge of Retrieval-Augmented Generation (RAG), embeddings, vector databases, prompt engineering, agentic workflows, tool/function calling, and model evaluation.
• Agentic AI & Automation: Experience designing, building, and operationalizing autonomous or semi-autonomous AI agents and workflows that securely integrate with enterprise systems, APIs, and data sources.
• Integration Development: Strong experience building REST APIs, microservices, event-driven integrations, and cloud-native services. Familiarity with API gateways and enterprise integration patterns.
• Programming: Proficiency in Python. Experience with Java, JavaScript/TypeScript, or similar enterprise programming languages is beneficial.
• AI Frameworks: Experience with frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, or equivalent AI orchestration frameworks.
• Data & Search: Experience integrating structured and unstructured enterprise data with GenAI solutions using vector search, semantic search, document ingestion, chunking, metadata management, and retrieval pipelines.
• DevOps / MLOps / LLMOps: Experience with CI/CD, Git, Docker, Kubernetes, Infrastructure as Code (Terraform, CloudFormation, Bicep, or equivalent), model deployment, monitoring, observability, and AI application lifecycle management.
• Security & Governance: Strong understanding of IAM, secrets management, encryption, API security, data privacy, Responsible AI, prompt-injection risks, PII protection, model governance, and enterprise security controls.
• Enterprise Integration: Ability to integrate GenAI capabilities with existing applications, APIs, databases, SaaS platforms, messaging systems, and internal enterprise services.
• Production Readiness: Experience designing scalable and resilient GenAI applications, including caching, rate limiting, fallback strategies, latency optimization, token and cost management, logging, monitoring, and error handling., * 5+ years of experience in software engineering, cloud engineering, enterprise integration, or related technology roles.
• 2+ years of hands-on experience developing or integrating Generative AI/LLM solutions.
• 2+ years of experience implementing security principles, controls, or security-focused solutions in enterprise environments.
• Demonstrated experience designing and delivering production-grade cloud and/or AI solutions.

Preferred Qualifications

• Relevant AWS, Microsoft Azure, or Google Cloud certifications.
• Experience delivering production-grade Generative AI and Agentic AI solutions.
• Knowledge of AI safety, LLM evaluation, guardrails, and Responsible AI practices.
• Experience working in highly regulated enterprise environments.
• Experience collaborating with cybersecurity, architecture, engineering, risk, and governance teams

• $101,700-173,200 per year