Software Engineer at NielsenIQ

Pune City, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

17 Aug, 26

Salary

0.0

Posted On

19 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, CrewAI, Vector Databases, RAG Pipelines, Prompt Engineering, API Design, Docker, Kubernetes, Scikit-learn, Agentic AI, Multi-agent Systems

Industry

Software Development

Description

Job Description About Job: AI Platform & Agentic Systems Engineer (AI / ML), Pune, India (Hybrid) Experience- 5+ years We are looking for a high-impact AI Platform & Agentic Systems Engineer who will help drive the AI transformation across our organization. This role goes beyond traditional machine learning development. The engineer will design and build AI-powered platforms, intelligent agents, orchestration layers, and enterprise AI workflows that enhance productivity, automate processes, and unlock intelligence across systems and data. You will work extensively with LLMs, SLMs, agentic AI architectures, vector databases, RAG pipelines, orchestration frameworks, and enterprise integrations to build scalable AI-enabled solutions. The ideal candidate is someone who actively builds with AI, understands modern AI ecosystems deeply, and can convert AI capabilities into real-world production systems. Key Responsibilities Enterprise AI Platform Development • Build and maintain internal AI platforms and services that enable teams across the organization to leverage AI capabilities • Develop reusable AI APIs, SDKs, and microservices for enterprise adoption • Integrate AI capabilities into existing systems, developer workflows, and business platforms • Enable organization-wide adoption of AI-assisted workflows and automation Agentic AI & Autonomous Systems • Design and implement agent-based architectures capable of multi-step reasoning and task execution • Build autonomous AI agents that interact with APIs, enterprise tools, and data systems • Develop multi-agent systems capable of collaboration and workflow orchestration • Implement tool-using agents that can dynamically select and use enterprise services Examples may include: AI engineering assistants workflow automation agents data intelligence agents internal knowledge agents AI Orchestration & Workflow Engines • Design AI orchestration layers that coordinate models, tools, and workflows • Build pipelines that manage: o multi-step reasoning o task decomposition o tool execution o memory and context management • Work with orchestration frameworks such as: o LangChain o LlamaIndex o CrewAI o similar orchestration frameworks • Enable structured interaction between LLMs, enterprise tools, APIs, and knowledge systems LLM / SLM Systems • Work with modern Large Language Models (LLMs) and Small Language Models (SLMs) • Evaluate and select models based on: o performance o latency o cost o reliability • Implement advanced techniques such as: o prompt engineering o prompt chaining o context window management o function calling o tool invocation Retrieval-Augmented Generation (RAG) • Design and implement RAG architectures for enterprise knowledge retrieval • Build pipelines that integrate LLMs with internal knowledge bases • Optimize retrieval systems to improve contextual accuracy and response relevance • Implement indexing pipelines and semantic retrieval strategies Vector Databases & Knowledge Infrastructure • Work with vector databases and embedding systems to enable semantic search • Implement knowledge indexing pipelines using: o Pinecone o Weaviate o Milvus o Chroma • Build scalable knowledge retrieval layers for AI applications Machine Learning & AI Model Development • Build ML pipelines using Python-based machine learning stacks • Work with ML frameworks including: o PyTorch o TensorFlow o Scikit-learn o Hugging Face ecosystem • Develop data pipelines, feature engineering workflows, and model training pipelines • Evaluate model performance and experiment with different architectures AI Infrastructure & Model Deployment • Deploy AI workloads in cloud and containerized environments • Build model inference pipelines and scalable AI services • Work with infrastructure tools such as: o Docker o Kubernetes o cloud AI platforms • Implement monitoring, evaluation, and lifecycle management for AI systems AI Development Environment & Tooling • Work within AI-enabled development environments • Leverage modern AI tooling including: o VS Code AI ecosystem o Copilot o AI coding assistants • Build internal tools that improve AI productivity across engineering teams AI Safety, Guardrails & Evaluation • Implement mechanisms to ensure safe and reliable AI outputs • Build guardrails to reduce hallucinations and unsafe responses • Develop AI evaluation frameworks and benchmarks • Monitor AI system performance and continuously improve reliability Qualifications Core AI / ML Engineering • 3–6 years experience in AI, Machine Learning, or Applied AI Engineering • Strong programming skills in Python • Experience with ML frameworks: o PyTorch o TensorFlow o Scikit-learn o Hugging Face Generative AI & LLM Systems • Hands-on experience working with LLM and SLM models • Experience integrating language models into applications and enterprise workflows • Strong understanding of: o prompt engineering o model evaluation o context management o tool integration Agentic AI • Experience building agent-based AI systems • Understanding of multi-agent architectures and tool-based reasoning • Experience with MCP servers or similar AI integration protocols AI Orchestration • Experience using orchestration frameworks such as: o LangChain o LlamaIndex o CrewAI o similar orchestration frameworks • Ability to build complex multi-step AI pipelines Retrieval Systems • Experience designing RAG-based AI systems • Experience with vector databases • Understanding of embedding models and semantic retrieval Software Engineering • Strong understanding of API design and service architectures • Experience building production-grade systems • Familiarity with Git, CI/CD workflows, and modern development practices What We Value Most • Builder mindset with strong hands-on experimentation • Ability to convert AI ideas into production-ready systems • Curiosity about emerging AI technologies • Passion for applying AI to real organizational transformation Additional Information 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

Responsibilities

Design and build AI-powered platforms, intelligent agents, and orchestration layers to automate enterprise workflows. Develop scalable RAG pipelines and integrate LLMs/SLMs into production-grade systems to enhance organizational productivity.