About this role
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 ingestionData preprocessingModel trainingEvaluationDeploymentMonitoringDevelop scalable workflow orchestration using tools such as:AirflowPrefectAzure ML PipelinesSageMaker PipelinesVertex AI PipelinesBuild and maintain automated CI/CD pipelines using:GitHub ActionsAzure DevOpsJenkinsAutomate 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 platformsCloud-native AI servicesImplement advanced deployment strategies including:Canary deploymentsBlue-Green deploymentsShadow deploymentsA/B testingSupport 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 latencyThroughputCost utilizationToken consumptionUser trafficService availabilityCreate dashboards and alerting frameworks using:PrometheusGrafanaDatadogAzure MonitorAWS CloudWatchDetect and resolve:Model driftData driftPerformance degradationInfrastructure incidents4. Cloud & Infrastructure Engineering
Operate and optimize AI workloads on at least one major cloud platform:Microsoft AzureAWSGoogle Cloud PlatformManage AI services such as:Azure DatabricksAzure OpenAIAWS SageMakerAmazon BedrockVertex AIBuild and maintain Infrastructure-as-Code using:TerraformCloudFormationARM/Bicep TemplatesProvision and manage:Compute clustersNetworkingStorageSecurity controlsManaged AI services5. Generative AI Orchestration & Vector Search
Build and maintain GenAI workflows using frameworks such as:LangChainLangGraphLangfuseLlamaIndexSemantic KernelSupport Retrieval-Augmented Generation (RAG) architectures.Develop and optimize:Embedding pipelinesVector database integrationsIndex refresh processesKnowledge retrieval systemsWork with vector databases including:PineconeWeaviateAzure AI SearchOpenSearchChromaDBFAISS6. 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 utilizationCompute efficiencyModel serving costsToken usageStorage consumptionRecommend 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 diagramsTechnical documentationRunbooksSOPsDeployment guidesOn-call support documentationEstablish 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 SQLMust be able to do Data Modelling Must know about Embeddings, Vector Database, Semantic SearchMust 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:DockerKubernetesContainer orchestrationProven expertise building CI/CD pipelines.Hands-on experience with Infrastructure-as-Code tools.Experience with monitoring and observability platforms.Working knowledge of:LLMsPrompt engineeringRAG architecturesVector databasesGenAI 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 environmentVolunteer time offLinkedIn LearningEmployee-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