Bases
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We are seeking a highly skilled GenAI MLOps Engineer to join our AI Engineering team.
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.
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.
1. ML Pipeline Engineering & CI/CD
Data ingestion
Data preprocessing
Model training
Evaluation
Deployment
Monitoring
Airflow
Prefect
Azure ML Pipelines
SageMaker Pipelines
Vertex AI Pipelines
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.
Kubernetes (AKS/EKS/GKE)
Serverless platforms
Cloud-native AI services
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.
Model latency
Throughput
Cost utilization
Token consumption
User traffic
Service availability
Prometheus
Grafana
Datadog
Azure Monitor
AWS CloudWatch
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
Azure Databricks
Azure OpenAI
AWS SageMaker
Amazon Bedrock
Vertex AI
Terraform
CloudFormation
ARM/Bicep Templates
Compute clusters
Networking
Storage
Security controls
Managed AI services
5. Generative AI Orchestration & Vector Search
LangChain
LangGraph
Langfuse
LlamaIndex
Semantic Kernel
Support Retrieval-Augmented Generation (RAG) architectures.
Embedding pipelines
Vector database integrations
Index refresh processes
Knowledge retrieval systems
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.
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
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).
Docker
Kubernetes
Container orchestration
Proven expertise building CI/CD pipelines.
Hands-on experience with Infrastructure-as-Code tools.
LLMs
Prompt engineering
RAG architectures
Vector databases
GenAI orchestration frameworks
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/
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
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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
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