Certified LLMOps Engineer CLOE

ADaSci Certified LLMOps Engineer (CLOE) Certification

The ADaSci Certified LLMOps Engineer (CLOE) program is a 30-hour, self-paced professional certification engineered to train developers, platform engineers, and AI architects to build, deploy, and scale production-grade Large Language Model infrastructure across cloud and on-premise enterprise environments.

While basic prompt engineering and building simple API wrappers have become commoditized, over 80% of enterprise Generative AI pilots stall before reaching production due to CISO security concerns, non-deterministic model drift, unmonitored agentic execution, and runaway token costs. The CLOE program bridges this operational bottleneck by providing end-to-end technical mastery across the full LLM deployment lifecycle.

Why Get Certified in LLMOps?

1. 30-Hour Self-Paced Mastery: Learn on your own schedule through structured modules, hands-on lab environments, and production-grade case studies.
2. Production Tool Stack Fluency: Gain direct execution experience with industry-standard platforms including Arize Phoenix, Langfuse, MLflow, Docker, Kubernetes, Weaviate, and multi-model AI gateways.
3. Lifetime Verifiable Credential: Earn an industry-recognized certification issued by ADaSci with a 1-click verifiable digital badge for LinkedIn and resume licensing.

Core Curriculum & Production Skills Mastered

Pillar 1: LLM System Architecture & Multi-Model Gateways

1. Focus: Building resilient, multi-provider abstraction layers.
2. Skills: Provider routing, fallback logic, rate-limit management, LiteLLM/Bifrost gateways, and sub-millisecond semantic caching to reduce API spend by 30%–70%.

Pillar 2: Prompt Version Control & Guardrails

1. Focus: Treating prompts as versioned software components.
2. Skills: GitFlow prompt versioning, structured JSON output enforcement (PydanticAI), OWASP input sanitization, and NVIDIA NeMo guardrails.

Pillar 3: Production RAG & Hybrid Vector Search

1. Focus: Operating high-throughput retrieval pipelines.
2. Skills: Weaviate and Qdrant managed vector stores, dense plus BM25 hybrid search, context-window optimization, and ingestion pipeline automation.

Pillar 4: Real-Time LLM Observability & Telemetry

1. Focus: Debugging non-deterministic model behavior and hallucination tracking.
2. Skills: Full-trace replay, token economics modeling, and evaluation dashboards using Arize Phoenix, Langfuse, and LangSmith.

Pillar 5: Automated Evaluation & Governance (LLM-as-a-Judge)

1. Focus: Replacing manual response testing with continuous automated scoring.
2. Skills: Building golden datasets, automated scoring harnesses (Ragas, DeepEval, Promptfoo), and regression testing before production rollouts.

Pillar 6: Cloud, On-Premise & Kubernetes Deployment

1. Focus: Securing enterprise workloads behind private infrastructure.
2. Skills: Containerized microservices using Docker, automated Jenkins/GitLab CI/CD pipelines, Kubernetes (Minikube/Helm) orchestration, and CISO compliance audit logging.

Who Should Enroll in CLOE?

This program is specifically designed for technical professionals looking to specialize in AI platform operations and infrastructure:

1. LLMOps & MLOps Engineers standardizing deployment pipelines, observability dashboards, and evaluation harnesses.
2. DevOps & SRE Professionals extending Docker containerization, Kubernetes orchestration, and CI/CD pipelines to AI workloads.
3. Backend & AI Engineers transitioning from local Jupyter notebooks and raw API calls to scalable production architectures.
4. Cloud & Platform Architects designing secure, private, and multi-cloud enterprise AI systems.
5. Tech Leads & Engineering Managers looking to reduce token costs, pass security audits, and accelerate time-to-production for AI initiatives.

Standard Certification vs. Pro Live Incubators