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.

Key Responsibilities

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.

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.

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

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

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

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.

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.

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.

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

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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

GenAI Engineer - Database