GenAI Engineer - Database
Core
Design, build, deploy, and operate core infrastructure for Generative AI and Machine Learning solutions, focusing on MLOps, model serving, and vector search systems.
Role type
Senior GenAI MLOps Engineer
Builds
Scalable, secure, and cost-effective AI operations including LLM-based applications, RAG systems, and automated ML pipelines.
Domain
Generative AI, Machine Learning Operations, Cloud Infrastructure
Deliverable
production ML models
Required skills
Cloud-native MLOps, Model deployment, CI/CD automation, Kubernetes, Infrastructure-as-Code, GenAI orchestration frameworks, Vector database integration, Data modeling, Scripting and automation
Preferred skills
GPU infrastructure optimization, Model evaluation frameworks, LLM observability tools, Responsible AI governance
Technologies
Airflow, Prefect, Azure ML Pipelines, SageMaker Pipelines, Vertex AI Pipelines, GitHub Actions, Azure DevOps, Jenkins, Docker, Kubernetes, Terraform, CloudFormation, ARM/Bicep, Prometheus, Grafana, Datadog, Azure Monitor, AWS CloudWatch, LangChain, LangGraph, Langfuse, LlamaIndex, Semantic Kernel, Pinecone, Weaviate, Azure AI Search, OpenSearch, ChromaDB, FAISS, Neo4j, memgraph
Responsibilities
Design and maintain end-to-end ML pipelines covering data ingestion, preprocessing, training, evaluation, and deployment; Implement observability for AI systems to monitor model latency, throughput, and drift; Operate and optimize AI workloads on major cloud platforms; Build and maintain GenAI workflows and vector search integrations; Implement secure AI deployment practices and cost optimization strategies.
Seniority
Senior, hands-on IC
