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NielsenIQ

Global provider of consumer purchasing data

Software Engineer - Python and AI/ML Development

AI Application Engineering +1Posted on Nov 13
Full-timeChennai, Tamil Nadu, IndiaNo salary listedHybrid

About the Job

Requirements

  • At least 3.5 years of strong working experience in Python development, including at least 1.5 years building machine-learning- or AI-enabled applications.
  • Solid experience with NumPy, Pandas, and scikit-learn for data-oriented services and model development.
  • Working experience with a deep-learning framework such as PyTorch or TensorFlow.
  • Hands-on experience with large language model APIs such as Azure OpenAI or similar, prompt engineering, embeddings, and vector databases.
  • Strong experience designing, building, and consuming REST APIs, and experience building and operating FastAPI or Flask services.
  • Strong experience with common software design patterns, such as Adapter, Strategy, and Factory.
  • Good experience using AI-assisted tools in the software development lifecycle.
  • Experience with code execution best practices, including memory footprint and profiling, and database query analysis, including explain plans and statistics.
  • Working knowledge of relational database management systems and NoSQL stores, and hands-on experience with messaging and queuing.
  • Proficiency in pytest and experience with API integration testing using Postman.
  • Experience with quality gates and security-tool integration in continuous integration and continuous delivery processes.
  • Hands-on experience with Docker, Kubernetes, and Azure cloud services.
  • Familiarity with Agile development methodologies.

Responsibilities

  • Design and develop maintainable, modular Python components and services that follow SOLID and equivalent principles.
  • Build, train, evaluate, and productionalize machine-learning models and AI components, including classical machine learning, natural language processing, and large language model-based features, in collaboration with product and architecture teams.
  • Develop large language model-powered capabilities such as prompt pipelines, retrieval-augmented generation, embeddings, and tool or function calling using frameworks such as LangChain or LlamaIndex.
  • Build data-processing and feature-engineering pipelines using NumPy, Pandas, and SQL.
  • Design, build, and consume REST APIs with FastAPI and Flask to serve models and AI capabilities.
  • Apply concurrency, asynchronous programming with asyncio, and batching techniques to improve inference and service throughput.
  • Track experiments, version models and datasets, and support model deployment using MLflow or equivalent machine-learning operations tooling.
  • Monitor production models and AI services for latency, cost, drift, and output quality, and act on findings.
  • Analyze technical problems, possible solutions, and their impact on the function and the business.
  • Provide informal guidance to new and junior team members through code reviews and pairing.
  • Understand how the team's work integrates with closely related teams and account for interdependencies.
  • Write and execute unit tests using pytest and mocking libraries, perform API integration testing, and conduct basic model-validation testing.
  • Implement continuous integration and continuous delivery pipelines to build and deploy applications, and maintain a high level of quality maturity in pipeline quality gates.
  • Implement security best practices, including input validation, authentication, authorization, secrets handling, and OWASP guidelines, as well as responsible AI practices such as prompt-injection awareness and data privacy.
  • Deploy and operate applications on Azure using Docker and Kubernetes.
  • Collaborate with cross-functional teams to define, design, and ship new features.
  • Troubleshoot and resolve issues in development, test, and production environments.
  • Explain technical, sometimes complex and dynamic concepts clearly to others.

About the Company

NielsenIQ

Global provider of consumer purchasing data

NielsenIQ collects and analyzes consumer purchasing data to help retailers and brands understand market behavior. It tracks what people buy, when, and where, using this data to measure market share and purchasing trends across categories. The product works by aggregating point-of-sale and other consumer data, then turning it into actionable insights and benchmarks that clients can use to optimize pricing, assortment, promotions, and marketing. NielsenIQ differentiates itself through its long history (dating back to 1923) and a broad global footprint (operating in over 100 countries), plus its expanded capabilities after merging with GfK in 2023 to strengthen data on technology and durable goods. Its goal is to provide objective, data-driven guidance that helps retailers and brands make informed decisions and improve performance.

Company size
10,001+
Company stage
IPO
Headquarters
Chicago, IL
Founded
1923

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