NielsenIQ: GenAI Engineer - Database — Pune, MH, India

About NielsenIQ

NielsenIQ, also known as NIQ, works in consumer intelligence and retail measurement. Its employer material describes using market information, consumer insights and analytics to help businesses understand buying behaviour. Its data operations address the accuracy and completeness of information behind client reports, while analytical services interpret that information. The business connects details about products and retail activity with broader views of markets, giving organisations evidence for understanding demand and making commercial decisions.

The position and its setting

NielsenIQ lists GenAI Engineer - Database in Pune, MH, India, under reference R26_0023552|IN. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Employment type: Full-time
• Employer listing date: 22 October 2026
• Brands: NielsenIQ
• Job Family: Data Science and Business Intelligence Generalist/Multidiscipline

Responsibilities and work described

• 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
• Collaboration: 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 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
• Design, build, and maintain end-to-end ML pipelines covering:
• Data ingestion
• Data preprocessing
• Model training
• Evaluation
• Deployment
• Monitoring
• Development: scalable workflow orchestration using tools such as:
• Airflow
• Prefect

Essential requirements and candidate background

• Knowledge retrieval systems
• 5+ years of experience in DevOps, Platform Engineering, SRE, or MLOps roles
• Minimum 3+ years supporting Machine Learning, Deep Learning, or AI production systems
• Proficiency: 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
• Hands-on expertise with one major cloud platform (Azure, AWS, or GCP)
• Experience deploying AI/ML workloads at scale
• Experience with:
• Docker
• Kubernetes
• Container orchestration
• Expertise building CI/CD pipelines
• Experience: Infrastructure-as-Code tools
• Experience: monitoring and observability platforms
• Working knowledge of:
• LLMs
• Prompt engineering
• RAG architectures

Preferred experience and additional strengths

• Experience working with Azure OpenAI, Amazon Bedrock, or Vertex AI
• Hands-on experience supporting production LLM applications
• Familiarity: GPU infrastructure and optimization
• Experience: model evaluation frameworks and LLM observability tools
• Knowledge: Responsible AI, AI governance, and security best practices
• Relevant cloud certifications (Azure, AWS, or GCP) are a plus

Location, schedule and contract conditions

• Work arrangement: On-site
• Advertised location: Pune, MH, India

Benefits mentioned by the employer

• 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/

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another GenAI Engineer - Database vacancy is not an offer for reference R26_0023552|IN; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to NielsenIQ

Applications follow the official employer page linked below.

Open the official NielsenIQ posting below and match the title and reference R26_0023552|IN.

Compare your qualifications and experience with the essential conditions for the Pune, MH, India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• SQL
• NoSQL
• Kubernetes
• CI/CD
• Docker
• AWS
• GCP
• Azure
• AI/ML
• LLMs
• Google Cloud
• Terraform
• machine learning