AI Platform & Agentic Systems Engineer

About the Position

AI Platform & Agentic Systems Engineer (AI / ML), Pune, India (Hybrid)

We are looking for a high-impact AI Platform & Agentic Systems Engineer who will help drive the AI transformation across our organization. With 5+ years of experience, the ideal candidate 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.

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

AI Orchestration & Workflow Engines

Design AI orchestration layers that coordinate models, tools, and workflows
Build pipelines that manage multi-step reasoning, task decomposition, tool execution, and memory and context management
Work with orchestration frameworks such as LangChain, LlamaIndex, CrewAI, and similar 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 performance, latency, cost, and reliability
Implement advanced techniques such as prompt engineering, prompt chaining, context window management, function calling, and 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 Pinecone, Weaviate, Milvus, Chroma, and similar tools
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 PyTorch, TensorFlow, Scikit-learn, and 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 Docker, Kubernetes, and 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 VS Code AI ecosystem, Copilot, and 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

Requirements

3–6 years experience in AI, Machine Learning, or Applied AI Engineering
Strong programming skills in Python
Experience with ML frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face
Hands-on experience working with LLM and SLM models
Experience building agent-based AI systems
Experience using orchestration frameworks such as LangChain, LlamaIndex, CrewAI, and similar frameworks
Strong understanding of API design and service architectures
Experience building production-grade systems
Familiarity with Git, CI/CD workflows, and modern development practices

Nice to Have

Experience designing RAG-based AI systems
Experience with vector databases
Understanding of embedding models and semantic retrieval

We Offer

Flexible working environment
Volunteer time off
LinkedIn Learning
Employee-Assistance-Program (EAP)

About the Company

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

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 Diversity Inclusion page.