Role Summary

We are partnering with a leading research and innovation organisation to hire a Junior/Senior Research Engineer to build Agentic Intelligence systems for planning, scheduling, and inventory optimization in manufacturing and operations environment. In this role, you will work on agent-based AI systems, combining Large Language Model (LLM), optimization techniques, and real-world data to create scalable, production-ready solutions.

Key Responsibilities

• Build and improve multi-agent systems for planning, scheduling, and inventory tasks

• Combine LLMs with optimization methods to solve complex real-world problems

• Design and extend semantic reasoning layers (intents, constraints, domain logic)

• Model operational data using knowledge graphs + vector search

• Develop event-driven backend services and APIs integrated with enterprise systems

• Improve agent orchestration, tool integration, and workflows

• Set up evaluation, monitoring, and guardrails (cost, latency, reliability)

• Deploy and run services using Docker, Kubernetes, and CI/CD pipelines

• Collaborate with AI/ML engineers to bring research into production

• Work with end users (planners/operators) to ensure practical, usable solutions

• Leverage AI coding tools to improve development speed and quality

Our Ideal Candidate

• Has a Bachelor's Degree in Computer Engineering, Software Engineering, or related field.

• Has at least 1-5 years of experience building production backend systems using Python, C#, FastAPI or similar, preferably in manufacturing domain with MES/ERP systems

• Has experience with the following tech stack:

  • Agent frameworks (e.g., LangChain, LangGraph)

  • RAG systems (retrieval, chunking, reranking, evaluation)

  • Vector databases (Pinecone, Qdrant, etc.)

  • Event-driven systems (Kafka or similar)

• Has strong knowledge of cloud (AWS/Azure/GCP), Docker, Kubernetes, CI/CD

• Has excellent problem-solving, troubleshooting, and analytical skills

• Has effective communication and collaboration skills
  
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