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. Key Responsibilities 1. ML Pipeline Engineering & CI/CD Design, build, and maintain end-to-end ML pipelines covering: Data ingestion Data preprocessing Model training Evaluation Deployment Monitoring Develop scalable workflow orchestration using tools such as: Airflow Prefect Azure ML Pipelines SageMaker Pipelines Vertex AI Pipelines Build and maintain automated CI/CD pipelines using: GitHub Actions Azure DevOps Jenkins Automate code quality checks, security scanning, testing, model validation, and deployment processes. 2. Model Deployment & Serving Containerize AI/ML workloads using Docker. Deploy and manage ML inference workloads on: Kubernetes (AKS/EKS/GKE) Serverless platforms Cloud-native AI services Implement advanced deployment strategies including: Canary deployments Blue-Green deployments Shadow deployments A/B testing Support deployment of LLMs, RAG systems, and AI agents into production environments. 3. Monitoring, Observability & Reliability Implement observability for AI systems through logs, metrics, and distributed tracing. Monitor: Model latency Throughput Cost utilization Token consumption User traffic Service availability Create dashboards and alerting frameworks using: Prometheus Grafana Datadog Azure Monitor AWS CloudWatch Detect and resolve: Model drift Data drift Performance degradation Infrastructure incidents 4. Cloud & Infrastructure Engineering Operate and optimize AI workloads on at least one major cloud platform: Microsoft Azure AWS Google Cloud Platform Manage AI services such as: Azure Databricks Azure OpenAI AWS SageMaker Amazon Bedrock Vertex AI Build and maintain Infrastructure-as-Code using: Terraform CloudFormation ARM/Bicep Templates Provision and manage: Compute clusters Networking Storage Security controls Managed AI services 5. Generative AI Orchestration & Vector Search Build and maintain GenAI workflows using frameworks such as: LangChain LangGraph Langfuse LlamaIndex Semantic Kernel Support Retrieval-Augmented Generation (RAG) architectures. Develop and optimize: Embedding pipelines Vector database integrations Index refresh processes Knowledge retrieval systems Work with vector databases including: Pinecone Weaviate Azure AI Search OpenSearch ChromaDB FAISS 6. Security, Governance & Compliance Implement secure AI deployment practices. Manage secrets and credentials using enterprise-grade security solutions. Ensure compliance with organizational security, governance, and data privacy standards. Apply role-based access control (RBAC), encryption, and audit logging practices. Support Responsible AI and model governance initiatives. 7. Cost Optimization & Performance Engineering Monitor cloud consumption and AI infrastructure costs. Optimize: GPU utilization Compute efficiency Model serving costs Token usage Storage consumption Recommend architectural improvements that improve scalability and reduce operational expenses. 8. Cross-Functional Collaboration Partner with Data Scientists and AI Engineers to productionize models. Collaborate with Software Engineering teams to integrate AI services into products. Participate in architectural reviews and technical design discussions. Support incident management and operational excellence initiatives. 9. Documentation & Operational Excellence Create and maintain: Architecture diagrams Technical documentation Runbooks SOPs Deployment guides On-call support documentation Establish best practices for AI platform operations and reliability. 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 specially Graph Db like Neo4j, memgraph (NosQL and SQL Must be able to do Data Modelling Must know about Embeddings, Vector Database, 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 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 GenAI orchestration frameworks Preferred Qualifications 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) are a plus. Our Benefits Flexible working environment Volunteer time off LinkedIn Learning Employee-Assistance-Program (EAP) NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://nielseniq.com/global/en/info/niqs-ai-safety-policies/ About NIQ NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population. For more information, visit NIQ.com Want to keep up with our latest updates? Follow us on: LinkedIn | Instagram | Twitter | Facebook Our commitment to Diversity, Equity, and Inclusion At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion