GenAI Engineer - Database

About NIQ

NIQ is a global leader in consumer intelligence, providing comprehensive insights into consumer purchasing behaviors and identifying new growth opportunities. In 2023, NIQ merged with GfK, expanding its global reach to cover over 90% of the world's population across 100+ markets. It operates as part of the Advent International portfolio, delivering advanced analytics via cutting-edge platforms to offer a full view of the retail landscape.

Role Overview

We are looking to add a highly experienced GenAI MLOps Engineer to our AI Engineering team. This role involves designing, building, deploying, and managing the key infrastructure that supports our Generative AI and Machine Learning solutions. You will work closely with data scientists, AI engineers, platform engineers, and software development teams to move LLM-based applications into production, automate processes, improve infrastructure, and ensure AI operations are scalable, secure, and cost-efficient.

Key Responsibilities

• Create and maintain comprehensive machine learning pipelines covering all phases from data ingestion through preprocessing, model training, evaluation, deployment, and monitoring.
• Develop scalable workflow orchestration using tools such as Airflow, Prefect, Azure ML Pipelines, SageMaker Pipelines, and Vertex AI Pipelines.
• Build and sustain automated CI/CD pipelines leveraging platforms like GitHub Actions, Azure DevOps, and Jenkins; automate code quality, security scans, testing, model validation, and deployment workflows.
• Containerize AI/ML workloads using Docker and manage deployment on Kubernetes (AKS, EKS, GKE), serverless platforms, and cloud-native AI services.
• Implement advanced deployment strategies including canary, blue-green, shadow deployments, and A/B testing to ensure robust production performance.
• Monitor AI system health with logs, metrics, and distributed tracing focusing on latency, throughput, cost utilization, token consumption, user traffic, and availability.
• Create alerting and visualization dashboards using Prometheus, Grafana, Datadog, Azure Monitor, and AWS CloudWatch.
• Detect and address model drift, data drift, performance degradation, and infrastructure issues promptly.
• Operate and optimize AI workloads on major cloud platforms such as Microsoft Azure, AWS, or Google Cloud, managing services like Azure Databricks, Azure OpenAI, AWS SageMaker, Amazon Bedrock, and Vertex AI.
• Develop and maintain Infrastructure-as-Code configurations using Terraform, CloudFormation, and ARM/Bicep templates, provisioning compute clusters, networking, storage, and security controls.
• Build GenAI orchestration workflows with frameworks including LangChain, LangGraph, Langfuse, LlamaIndex, and Semantic Kernel, supporting Retrieval-Augmented Generation (RAG) architectures.
• Develop, optimize, and manage embedding pipelines, vector database integrations, index refresh processes, and knowledge retrieval systems using vector databases such as Pinecone, Weaviate, Azure AI Search, OpenSearch, ChromaDB, and FAISS.
• Enforce secure AI deployment practices, manage secrets and credentials with enterprise-grade security solutions, and uphold compliance with organizational security and privacy standards.
• Implement role-based access control, encryption, and audit logging practices supporting responsible AI and governance initiatives.
• Monitor and optimize cloud and AI infrastructure costs, including GPU utilization, compute efficiency, model serving, token usage, and storage consumption; propose architectural enhancements for scalability and cost reduction.
• Collaborate with data scientists, AI engineers, and software teams to integrate AI models into products and participate in architecture and design discussions.
• Contribute to incident management and operational excellence efforts.
• Develop detailed architecture diagrams, technical documentation, runbooks, standard operating procedures, deployment guides, and on-call support materials to promote operational best practices and reliability.

Qualifications

• Minimum 5 years of experience in DevOps, Platform Engineering, Site Reliability Engineering (SRE), or MLOps roles.
• At least 3 years supporting production systems involving machine learning, deep learning, or AI.
• Proficiency with databases, particularly graph databases like Neo4j and Memgraph, including both NoSQL and SQL data modeling.
• Strong knowledge of embeddings, vector databases, and semantic search concepts.
• Skills in scripting and automation using Python, Golang, Bash, or similar languages.
• Expertise operating on one major cloud environment—Azure, AWS, or GCP—deploying AI/ML workloads at scale.
• Hands-on experience with Docker, Kubernetes, container orchestration, and building CI/CD pipelines.
• Proven ability using Infrastructure-as-Code tools and monitoring/observability platforms.
• Working knowledge of large language models (LLMs), prompt engineering, RAG architectures, vector databases, and GenAI orchestration frameworks.

Preferred Qualifications

• Experience with Azure OpenAI, Amazon Bedrock, or Vertex AI platforms.
• Direct hands-on experience supporting LLM applications in production.
• Familiarity with GPU infrastructure and optimization techniques.
• Experience with model evaluation frameworks and LLM observability tools.
• Understanding of responsible AI, governance, and security best practices.
• Relevant cloud certifications from Azure, AWS, or GCP are considered a plus.

Eligibility

The position is open to candidates who hold any graduate degree.