FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Senior MLOps/LLMOps Engineer, Development
Citrin CoopermanSenior MLOps/LLMOps Engineer at Citrin Cooperman developing automated pipelines for generative AI applications. Leading the deployment and management of AI evaluation infrastructure and monitoring.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and building automated deployment pipelines for generative AI applications, with a strong focus on CI/CD processes, infrastructure management, and operational metrics instrumentation. Proficient in leveraging cloud infrastructure and LLMOps tools to ensure secure and efficient AI solution deployment.
Highest-signal resume keywords
CI/CD Pipeline DevelopmentGenerative AI DeploymentAzure DevOps Engineer ExpertPython ScriptingMLOps Tools and Frameworks
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
CI/CD Pipeline DevelopmentPython ScriptingContainerization (Docker, Kubernetes)Infrastructure ManagementVersion Control for AI ModelsOperational Metrics InstrumentationInput/Output Guardrails ImplementationFinancial Monitoring of AI SolutionsAutomation PrinciplesAPI Ecosystem Understanding
Soft Skills
Analytical DefenderFinancially VigilantAutomation-Obsessed
Tools & Technologies
Azure DevOpsGitHub ActionsGitLab CIMLflowLangSmithPromptFlowArizeOpenAIAnthropicGoogle Vertex AI
Certifications & Qualifications
Databricks Certified: Machine Learning ProfessionalMicrosoft Certified: Azure DevOps Engineer Expert (AZ-400)DeepLearning.AI: Machine Learning Engineering for Production (MLOps)
Industry Keywords
Generative AIMLOpsSite Reliability Engineering (SRE)Infrastructure-as-CodeMulti-Agent Frameworks
Tech Stack
Tools & technologiesAWSAzureCloudDockerKubernetesPython
About the role
Key responsibilities & impact- Design and build automated deployment pipelines specifically for generative AI applications
- Ensure that updates can be safely promoted across environments (Dev, Test, Prod)
- Deploy and manage the infrastructure required for continuous AI evaluation
- Instrument AI applications to capture deep operational metrics
- Implement version control for prompts and model configurations
- Integrate input/output guardrails into the application flow to automatically block prompt injection attacks, PII leakage, or off-topic responses
- Actively monitor the financial footprint of AI solutions
Requirements
What you’ll need- Bachelor’s degree in computer science, information technology, engineering, or equivalent practical experience
- Databricks Certified: Machine Learning Professional
- Microsoft Certified: Azure DevOps Engineer Expert (AZ-400)
- DeepLearning.AI: Machine Learning Engineering for Production (MLOps)
- 4+ years of experience in DevOps, MLOps, or Site Reliability Engineering (SRE) with specific experience in generative AI deployments in last 1-2 years
- Deep proficiency in building CI/CD pipelines using enterprise tools (Azure DevOps, GitHub Actions, GitLab CI)
- Hands-on experience with LLMOps tools and frameworks (e.g., MLflow, LangSmith, PromptFlow, Arize, or similar observability platforms)
- Strong Python scripting skills and experience containerizing machine learning or API workloads (Docker, Kubernetes)
- Understanding of the API ecosystems for frontier models (OpenAI, Anthropic, Google Vertex AI) and multi-agent frameworks (LangChain, LangGraph)
- Familiarity with cloud infrastructure (Azure, AWS) and infrastructure-as-code principles
- Automation-obsessed
- Financially vigilant
- Analytical defender
Benefits
Comp & perks- Competitive compensation and benefits
- Flexibility to manage personal and professional life