AI Engineer (Mid‑Level) - 3 to 6 years at NielsenIQ

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

14 May, 26

Salary

0.0

Posted On

13 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Generative AI, LLM, PyTorch, TensorFlow, HuggingFace, LangChain, LlamaIndex, Vector Databases, MLOps, CI/CD, AWS, Azure, GCP, Kubernetes

Industry

Software Development

Description

Company Description 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. Job Description AI Engineer (Mid‑Level) We’re looking for a Mid-Level AI Engineer who is passionate about building secure, scalable, and production‑ready AI systems. In this role, you’ll design, deploy, and optimize machine learning and generative AI solutions that power analytics, automation, and intelligent products. You’ll work across Python, cloud platforms, vector databases, LLM frameworks, and modern MLOps practices — treating testing, reliability, and security as foundational pillars. Key Responsibilities AI/ML Model Development Build and optimize machine learning and LLM-based models (classification, NLP, embeddings, generative AI). Develop reusable model pipelines in Python using libraries like scikit‑learn, PyTorch, TensorFlow, or HuggingFace. Implement feature engineering, vectorization, and model evaluation frameworks. Generative AI & LLM Engineering Fine‑tune, evaluate, and deploy LLMs using frameworks such as HuggingFace Transformers, LangChain, LlamaIndex, or cloud-native AI services. Build RAG (Retrieval-Augmented Generation) pipelines using vector databases (Pinecone/Weaviate/FAISS/Snowflake Cortex Search). Implement prompt engineering, prompt orchestration, and prompt quality testing. MLOps & CI/CD Build automated training, deployment, and monitoring pipelines using CI/CD (GitHub Actions/Azure DevOps/GitLab CI). Manage model versioning, lineage, and artifact tracking (MLflow, Weights & Biases). Implement reproducibility, environment management, and dependency controls. Cloud & Deployment Deploy AI services to AWS/Azure/GCP using serverless compute or containerized microservices (Lambda, Azure Functions, Cloud Run, Kubernetes). Optimize compute workloads, costs, and auto-scaling strategies. Integrate models into production systems via APIs, SDKs, or event-driven flows. Security, Reliability & Observability Apply DevSecOps practices: secure model endpoints, manage secrets, enforce least‑privilege IAM. Implement monitoring for drift, model decay, model failures, and performance anomalies. Contribute to logging, metrics, dashboards, and alerting for AI workloads. Collaboration & Documentation Partner with Data Engineering, Product, and Analytics teams to integrate models into applications and data pipelines. Write clear documentation (model cards, evaluation reports, design documents, runbooks). Qualifications Must‑Have 3–6 years of experience in AI engineering, ML engineering, or backend engineering with AI/ML exposure. Strong Python skills: pandas, NumPy, pydantic, async patterns, testing (pytest). Experience with ML frameworks: scikit‑learn + at least one deep learning/LLM library (PyTorch/TensorFlow/HuggingFace). Cloud proficiency with AWS, Azure, or GCP (compute, storage, IAM basics). Hands-on experience deploying ML/AI models to production environments. Understanding of vector databases, embeddings, and retrieval techniques. Experience with Git, CI/CD, code reviews, dependency scanning, and secure coding. Knowledge of model evaluation techniques (regression, classification, NLP metrics, LLM evals). Nice‑to‑Have RAG workflows using LangChain/LlamaIndex. Experience with MLOps tooling: MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML. Experience with containerization & orchestration: Docker, Kubernetes. Exposure to data engineering concepts (ETL/ELT, Snowflake, dbt). Knowledge of SCD/CDC concepts and dimensional modeling. Understanding of AI security practices: input validation, model hardening, secret stores. Monitoring tools (Prometheus, Datadog, CloudWatch, Log Analytics). Familiarity with responsible AI concepts, fairness, and bias mitigation. Additional Information Enjoy a flexible and rewarding work environment with peer-to-peer recognition platforms. Recharge and revitalize with help of wellness plans made for you and your family. Plan your future with financial wellness tools. Stay relevant and upskill yourself with career development opportunities Exciting work environment that brings people together. Use the latest digital technologies. Ongoing trainings to support your development. Opportunities for personal and professional growth. Great compensation and bonus scheme linked to individual performance and company results. Flexible working hours and home office. 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://www.nielseniq.com/global/en/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 Career Site Team: Technology

Responsibilities

The role involves designing, deploying, and optimizing machine learning and generative AI solutions, focusing on building secure, scalable, and production-ready AI systems. Key tasks include developing and optimizing models, building RAG pipelines using vector databases, and implementing robust MLOps and CI/CD practices.