UGV Systems Architect responsible for the system architecture of autonomous vehicles at Daimler Truck. Integrating UGV solutions into existing vehicle platforms and digital ecosystems.
MI
Hybrid Cloud & AI Systems Engineer
Posted 4 months ago
About the role
- Cloud & AI Systems Engineer designing and optimizing Azure cloud infrastructure for health care solutions provider. Collaborating with teams on AI/ML workloads and implementing security and compliance measures.
Responsibilities
- Lead the design, implementation, and continuous improvement of Microsoft Azure cloud technologies (IaaS, PaaS, SaaS)
- Develop Azure policies, ARM templates, Blueprints, and governance strategies
- Build and maintain secure, scalable, and cost-optimized infrastructure solutions
- Drive infrastructure automation and self-service provisioning through Infrastructure-as-Code (IaC), scripting, and DevOps pipelines
- Monitor system performance, troubleshoot incidents, and ensure high availability and disaster recovery readiness
- Collaborate in cross-functional teams (e.g., with Finance, Data Science, Software Engineering and Security) to design, deploy, and maintain secure and scalable AI/ML pipelines
- Support retrieval-augmented generation (RAG) pipelines by implementing and maintaining secure endpoints, network isolation, and proper access controls
- Manage API security, authentication, and token usage for AI and LLM-based services, ensuring visibility into utilization and cost tracking
- Deploy and monitor machine learning models and APIs using Azure ML, Azure OpenAI, and containerized inference solutions
- Support AI DevOps/MLOps automation using Bicep, Terraform, or MLflow to streamline model deployment, configuration, and lifecycle management
- Implement monitoring and observability using Azure Monitor, Application Insights, and related tools to track AI API performance, latency, and errors
- Assist in operationalizing AI workloads in Azure ML, OpenAI, or custom inference endpoints while ensuring security and compliance alignment
- Contribute to AI governance by helping define secure model lifecycle processes, role-based access controls, and compliance checkpoints across environments
- Implement and manage AI-specific security controls, such as prompt injection mitigation, data masking in training datasets, and LLM access policies
- Collaborate with security team to evaluate and secure AI model endpoints, ensuring compliance with HIPAA and SOC 2 for AI workloads
- Support the development and enforcement of cloud security policies and incident response plans
- Collaborate with the security team to investigate cyber threats and deploy AI-driven threat detection and response tools
- Ensure compliance with industry standards (e.g., HIPAA, SOC 2, HITRUST) in all cloud and AI implementations
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience)
- 5–7 years of experience with Microsoft Azure cloud architecture and automation, including design, deployment, and optimization of cloud environments, with exposure to AI/ML production systems
- Proven ability to build and maintain AI-native pipelines, including data ingestion, embeddings, inference endpoints, and vector databases
- Hands-on experience with Python for automation, scripting, and AI integration
- Familiarity with LLM application frameworks (e.g., LangChain, Semantic Kernel) and MLOps platforms (e.g., Azure ML Pipelines, MLflow, Kubeflow)
- Strong understanding of data engineering fundamentals, model deployment workflows, and AI observability tools (e.g., Weights & Biases, Arize, PromptLayer)
- Proficiency with Infrastructure-as-Code (IaC) tools such as Terraform, ARM templates, or Bicep
- Expertise in containerization and orchestration technologies (e.g., Docker, Kubernetes) for AI and cloud workloads
- Solid knowledge of Active Directory, Windows Server OS, VMware, networking, and hybrid cloud environments
- Demonstrated experience implementing DevOps automation and CI/CD pipelines (e.g., Azure DevOps)
- Knowledge of cloud cost optimization tools and methodologies
- Experience working in Agile/Scrum environments
- Excellent problem-solving, troubleshooting, root cause analysis, and performance optimization skills
- Strong documentation, communication, and collaboration skills with a customer-centric mindset
- Ability to translate business requirements into secure, scalable, and cost-efficient technology solutions
- Curiosity and willingness to experiment with generative AI, automation, and emerging technologies
- Experience contributing to AI strategy, governance, or ethical AI initiatives is a plus
Benefits
- Comprehensive medical, dental, vision and prescription plans with FSA/HSA options individual and family options
- Teledoc access
- Fitness Reimbursement
- Commuter Benefit Plan
- Access to an Employee Assistance Program (EAP)
- Paid Time Off
- Day off for your birthday and a floating holiday
- Paid Parental Leave
- 401K with a match
- Employee Stock Purchase Plan
- Life Insurance, short-term & long-term disability insurance
- Access to financial and legal advisors
- Tuition Reimbursement
- E-learning programs
- Ongoing Team Trainings
- Paid volunteer time-off
- Donation matching
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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