Senior Machine Learning Engineer – MLOps & LLMOps

At ZoomInfo we turn billions of data points into instant go-to-market answers for 40,000+ customers. To keep that edge, we’re building an elite Applied AI platform that scales from first experiment to planet-scale production—securely, cost-effectively, and fast. Join us as a Senior Machine Learning Engineer and own the MLOps & LLMOps backbone that powers our retrieval, recommendation, and knowledge-graph products.

What you will do:

• Architect the end-to-end MLOps / LLMOps stack (Kubernetes, Ray, Argo, Terraform, MLFlow, Feature & Model Stores) for training, fine-tuning, and serving LLMs, RAG pipelines, NER, and entity-resolution models at multi-billion-record scale. 
• Design cost-aware training & inference workflows—automatic mixed precision, quantization, distillation, dynamic batching, on-demand GPU/CPU autoscaling—to cut $/call and CO₂ while meeting 99.9% SLAs. 
• Build iron-clad evaluation & safety frameworks: red-teaming, hallucination scoring, PII/toxicity filters, guardrail policies, shadow deployments, and continuous regression tests. 
• Fine-tune embedding models and pre-train domain-specific LLMs on ZoomInfo corpora; integrate with vector databases (Pinecone, Milvus, OpenSearch) to power RAG search and recommender systems.
• Prototype and benchmark emerging AI/infra tech (parameter-efficient tuning, retrieval frameworks, serverless GPUs) against incumbent solutions; present clear “buy-build-borrow” recommendations. 
• Model, build, and optimize knowledge graphs—schema design, entity linking, incremental updates—to supercharge retrieval and recommendation accuracy. 
• Profile and re-architect RAG, NER, and entity-resolution services for sub-100 ms latency, 5× QPS jumps, and zero-downtime deploys across global regions. 
• Lead multi-team CI/CD best practices—blue/green, canary, GitOps—and large-scale A/B tests to quantify revenue impact. 
• Mentor engineers, publish internal playbooks & external blogs, and speak at industry forums to cement ZoomInfo’s technical leadership.

What you bring:

• 7+ years building production ML systems (or 4+ post-PhD/MS) with at least two at 100M+ user or record scale. 
• Deep expertise in modern MLOps/LLMOps (K8s, Docker, MLFlow/Vertex AI/SageMaker, Kubeflow, Airflow, ArgoCD, model registries). 
• Proven record optimizing LLMs/SLMs via quantization, LoRA/PEFT, distillation, and distributed training (DeepSpeed, FSDP, Hugging Face, TensorRT-LLM). 
• Strong software-engineering fundamentals in Python and experience with distributed computing infrastructure such as Spark and Ray based jobs and pipelines
• Hands-on mastery of vector search & RAG patterns, entity-resolution algorithms, and graph data stores (Neo4j, Neptune, JanusGraph, Spanner Graph) is a major plus. 
• Fluency in cost, reliability, security, and compliance trade-offs; comfortable providing inputs to budgets and setting SLOs. 
• Exceptional communication skills—able to align executives, PMs, Data Scientists and engineers around data-driven decisions.

Why ZoomInfo?

• Own infrastructure that touches billions of documents, millions of daily requests, and nine-figure revenue lines. 
• Green-field influence: choose the stack, set the guardrails, and define best practices that shape our next-gen AI platform. 
• Culture that rewards initiative, collaboration, ships quickly, invests heavily in professional growth.
• Competitive pay, equity, and benefits; flexible location (US hubs, or remote within US and Canada. Candidate needs to be living in the USA or Canada).

Ready to industrialize AI at scale? Apply now and build the backbone of ZoomInfo’s next era of intelligent products.

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