Job highlights

- Typically requires a Bachelor s degree,preferably in computer science,Artificial Intelligence,Data Science,mathematics,or a similar field with quantitative coursework,and 4-5 years of professional experience utilizing quantitative analysis,a Master s degree and 2-3 years of experience.
- Required Skills
- Supervised,Unsupervised,Reinforcement learning experience is a plus).

Job Summary Build the Digital Backbone of Modern Manufacturing. We are assembling a agile team to develop and scale our Manufacturing Supply Digital Platform-a next-generation software framework that transforms how manufacturing and supply operations connect, collaborate, and optimize. This platform is not an ERP system.

It's a purpose-built digital layer that integrates data, processes, and resources across the entire manufacturing lifecycle-from design and engineering to production and distribution. This initiative is powered by NVIDIA technologies, including the Omniverse platform and AI computing capabilities, enabling immersive digital twins, accelerated simulation, and intelligent automation. You'll be part of a team that's not just building software-but shaping the future of how manufacturing works through AI-driven, collaborative, and scalable digital solutions.

As part of this initiative, you'll contribute to:

- System Integration: Seamlessly connecting diverse manufacturing and supply systems, data sources, and workflows into a unified digital ecosystem.
- Data-Driven Decision Making: Harnessing real-time data collection, analysis, and visualization to deliver actionable insights and operational intelligence.
- Automation Optimization: Driving efficiency through intelligent scheduling, AI Agents, predictive maintenance, and quality control-without replacing core transactional systems.
- Enhanced Collaboration: Enabling transparent communication and coordination across teams, functions, and geographies.

Key Responsibilities

- Build, integrate, and optimize AI Assistants and agent-based solutions capable of retrieving information, executing workflows,



and supporting business processes through natural language interactions.
- Develop and maintain integrations with enterprise data sources, APIs, knowledge bases, document repositories, and business applications to enable accurate and context-aware AI experiences.
- Implement prompt engineering, grounding, retrieval, memory management, and agent orchestration techniques to improve response quality, reliability, and user experience.
- Develop document ingestion, metadata extraction, chunking, indexing, and knowledge management pipelines to support enterprise search and AI Assistant capabilities.
- Evaluate, test, and optimize AI solutions using techniques such as prompt tuning, model selection, caching, reranking, and retrieval optimization to improve accuracy, latency, and cost efficiency.
- Collaborate with product, engineering, and domain teams to define requirements, translate business needs into AI capabilities, and deliver production-ready solutions.
- Implement observability, monitoring, evaluation, and feedback mechanisms to measure AI Assistant performance, identify issues, and drive continuous improvement.
- Support deployment, productionization, and operational readiness activities, including security, entitlements, access controls, auditability, and governance requirements.
- Stay current with advancements in LLMs, agent frameworks, RAG architectures, vector databases, knowledge graphs, and AI platform technologies, and contribute to the adoption of relevant capabilities.

Required Skills

- LangGraph, Semantic Kernel, CrewAI, AutoGen or similar agentic AI frameworks.
- Familiarity with Retrieval-Augmented Generation (RAG) architectures, vector databases (Milvus, Pinecone)



and enterprise knowledge base implementations.
- Experience building and deploying AI Assistants, Copilots, or conversational AI solutions for business or operational use cases.
- Exposure to agent orchestration, tool calling, workflow automation, and multi-agent systems.
- Deep expertise in Python especially with PyTorch/TensorFlow (model training, fine-tuning for vision, text, and audio; Supervised, Unsupervised, Reinforcement learning experience is a plus).
- Knowledge of Machine Learning techniques: anomaly detection, predictive analytics

Nice to Have Skills

- Knowledge of document ingestion, OCR, metadata extraction, and document processing frameworks such as Textract, Docling, or equivalent technologies.
- Understanding of LLM evaluation, observability, prompt engineering, and responsible AI practices.
- Familiarity with Graph databases, Knowledge Graphs, and Ontology-based retrieval and reasoning.
- Experience integrating AI solutions with enterprise platforms through REST APIs, FastAPI, microservices, and event-driven architectures.
- Exposure to cloud-based AI services such as Azure OpenAI, AWS Bedrock, Amazon Textract, Azure AI Foundry, or similar platforms.
- Knowledge of security concepts including OAuth, OIDC, RBAC, entitlements, auditability, and enterprise AI governance.

Educational Background Typically requires a Bachelor s degree, preferably in computer science, Artificial Intelligence, Data Science, mathematics, or a similar field with quantitative coursework, and 4-5 years of professional experience utilizing quantitative analysis, a Master s degree and 2-3 years of experience.

This position requires working onsite five days a week.

Additional Information

Visa Sponsorship is not available for this position.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Engineer (Chennai)
🏢 Caterpillar
📍 Chennai