GenAI Engineer - Database

We are seeking a highly skilled GenAI MLOps Engineer to join our AI Engineering team. In this role, you will design, build, deploy, and operate the core infrastructure powering our Generative AI and Machine Learning solutions. You will collaborate closely with Data Scientists, AI Engineers, Platform Engineers, and Software Development teams to productionize LLM-based applications, automate workflows, optimize infrastructure, and ensure scalable, secure, and cost-effective AI operations. The ideal candidate possesses strong expertise in cloud-native MLOps, model deployment, CI/CD automation, Kubernetes, Infrastructure-as-Code, and modern GenAI orchestration frameworks.

Responsibilities

Design, build, and maintain end-to-end ML pipelines (data ingestion, preprocessing, model training, evaluation, deployment, monitoring); Develop scalable workflow orchestration using Airflow, Prefect, Azure ML Pipelines, SageMaker Pipelines, or Vertex AI Pipelines; Build and maintain automated CI/CD pipelines using GitHub Actions, Azure DevOps, or Jenkins; Containerize AI/ML workloads using Docker; Deploy and manage ML inference workloads on Kubernetes, serverless platforms, and cloud-native AI services; Implement advanced deployment strategies (canary, blue-green, shadow, A/B testing); Support deployment of LLMs, RAG systems, and AI agents into production; Implement observability for AI systems and monitor latency, throughput, cost, token consumption, and availability; Detect and resolve model drift, data drift, performance degradation, and infrastructure incidents; Operate and optimize AI workloads on Azure, AWS, or Google Cloud Platform; Build and maintain Infrastructure-as-Code using Terraform, CloudFormation, or ARM/Bicep; Build and maintain GenAI workflows and RAG architectures, including embedding pipelines and vector database integrations; Implement secure AI deployment practices, secrets management, RBAC, encryption, and audit logging; Monitor and optimize cloud consumption, GPU utilization, and AI infrastructure costs; Partner with Data Scientists and AI Engineers to productionize models; Collaborate with Software Engineering teams to integrate AI services into products; Create and maintain architecture diagrams, technical documentation, runbooks, SOPs, and deployment guides

Requirements

5+ years of experience in DevOps, Platform Engineering, SRE, or MLOps roles; Minimum 3+ years supporting Machine Learning, Deep Learning, or AI production systems; Proficient in databases, especially graph databases like Neo4j and Memgraph (NoSQL and SQL); Must be able to do data modelling; Must know about embeddings, vector databases, and semantic search; Must have scripting and automation skills using Python, Golang, Bash, or similar languages; Strong hands-on expertise with one major cloud platform (Azure, AWS, or GCP); Experience deploying AI/ML workloads at scale; Strong experience with Docker, Kubernetes, and container orchestration; Proven expertise building CI/CD pipelines; Hands-on experience with Infrastructure-as-Code tools; Experience with monitoring and observability platforms; Working knowledge of LLMs, prompt engineering, RAG architectures, vector databases, and GenAI orchestration frameworks

Nice to have

Experience working with Azure OpenAI, Amazon Bedrock, or Vertex AI; Hands-on experience supporting production LLM applications; Familiarity with GPU infrastructure and optimization; Experience with model evaluation frameworks and LLM observability tools; Knowledge of Responsible AI, AI governance, and security best practices; Relevant cloud certifications (Azure, AWS, or GCP)

Benefits

Flexible working environment; Volunteer time off; LinkedIn Learning; Employee-Assistance-Program (EAP)