AI Solutions Engineer

Nxera; Singapore; On-site; Confidential; Full time

Job summary

Nxera seeks an AI Solutions Engineer to design, build and deploy enterprise AI solutions using LLMs, RAG and AI Agents. You will translate business requirements into practical AI systems, delivering secure, scalable and trustworthy AI experiences across knowledge-intensive workflows.

Responsibilities include developing production-ready AI applications, reusable prompt workflows, and integration with enterprise APIs and databases, while collaborating with product and engineering teams for

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science or related discipline.
• 3–5 years of software engineering experience with Python.
• Hands-on experience building LLM applications, AI Agents or RAG solutions.
• Experience with LangChain, LangGraph, LlamaIndex or similar AI frameworks.
• Experience integrating APIs, databases and enterprise systems.
• Knowledge of vector databases, semantic search and prompt engineering.
• Experience with Git, CI/CD and container technologies.

Responsibilities

• Design, develop and deploy AI agents using LLMs, RAG and prompt engineering.
• Build scalable AI workflows that automate enterprise business processes.
• Translate business requirements into practical AI solutions.
• Develop reusable prompt workflows, tool-calling capabilities and structured outputs.
• Build and optimize RAG pipelines connected to approved enterprise knowledge sources.
• Improve retrieval quality through chunking, embeddings, indexing and metadata strategies.
• Maintain trusted knowledge bases and ensure source-grounded AI responses.
• Integrate AI applications with enterprise systems, APIs, databases and internal platforms.
• Develop secure tool-calling capabilities and support deployment into production.
• Monitor and optimize AI application performance.
• Design evaluation frameworks to measure response quality, retrieval accuracy and hallucination risks.
• Optimize prompts, guardrails and model performance.
• Support governance, version control and human-in-the-loop review processes.
• Partner with product, engineering and business teams to deliver AI solutions.
• Support demonstrations, UAT, production rollout and technical documentation.
• Communicate technical concepts clearly to technical and non-technical stakeholders.

Skills

Python

LLM applications

AI Agents

RAG solutions

API integration

CI/CD

Container technologies

Vector databases

Prompt engineering

Education

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science or related discipline

Tools

LangChain

LangGraph

LlamaIndex

Git

CI/CD tooling

Docker

Job description

Weare seeking an AI Solutions Engineer (Agentic AI) to design, build and deploy enterprise AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and AI Agents. You will develop production-ready AI applications that automate knowledge-intensive workflows, integrate with enterprise systems and deliver secure, scalable and trustworthy AI experiences.

How You will Make An Impact:

AI Solution Development

• Design, develop and deploy AI agents using LLMs, RAG and prompt engineering.
• Build scalable AI workflows that automate enterprise business processes.
• Translate business requirements into practical AI solutions.
• Develop reusable prompt workflows, tool-calling capabilities and structured outputs.

Knowledge & RAG Engineering

• Build and optimize RAG pipelines connected to approved enterprise knowledge sources.
• Improve retrieval quality through chunking, embeddings, indexing and metadata strategies.
• Maintain trusted knowledge bases and ensure source-grounded AI responses.

AI Platform & Integration

• Integrate AI applications with enterprise systems, APIs, databases and internal platforms.
• Develop secure tool-calling capabilities and support deployment into production.
• Monitor and optimize AI application performance.

Model Quality & Governance

• Design evaluation frameworks to measure response quality, retrieval accuracy and hallucination risks.
• Optimize prompts, guardrails and model performance.
• Support governance, version control and human-in-the-loop review processes.

Stakeholder Collaboration

• Partner with product, engineering and business teams to deliver AI solutions.
• Support demonstrations, UAT, production rollout and technical documentation.
• Communicate technical concepts clearly to technical and non-technical stakeholders.

Skills for Success:

• Bachelor's Degree in Computer Science, Artificial Intelligence, Data Science or related discipline.
• 3–5 years of software engineering experience with Python.
• Hands-on experience building LLM applications, AI Agents or RAG solutions.
• Experience with LangChain, LangGraph, LlamaIndex or similar AI frameworks.
• Experience integrating APIs, databases and enterprise systems.
• Knowledge of vector databases, semantic search and prompt engineering.
• Experience with Git, CI/CD and container technologies.

Preferred Skills:

• Experience with Azure OpenAI, AWS Bedrock or Google Vertex AI.
• Knowledge of MCP (Model Context Protocol) or AI agent orchestration.
• Experience deploying open-source LLMs (e.g. vLLM, Ollama).
• Exposure to MLOps, modelfine-tuning or domain adaptation.