#EG AI Developer (Agentic AI Systems)

Ncs3; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

NCS is seeking an AI Developer to own the intelligent layer of our bespoke enterprise system, building robust, secure AI agents for high-consequence workflows. You will design stateful multi-agent networks, enhance RAG capabilities, and deploy AI microservices in OCI-compliant environments with strong guardrails and CI/CD integration.

You will collaborate with software teams to ensure alignment with business objectives and technical requirements, while delivering reliable, observable AI

Qualifications

• Expert-level proficiency in Python, specifically for asynchronous AI applications.
• Mastery of LangGraph and LangChain for building production-grade, stateful systems with human-in-the-loop verification patterns.
• Proven success in developing RAG capabilities, including chunking strategies, query expansion, re-ranking, hybrid search, etc.
• Deep hands-on experience with vector DB, graph DB, NoSQL, and RDBMS.
• Experience building guardrails to mitigate risks such as prompt injection and data leakage.
• Experience integrating AI evaluation into CI/CD pipelines (e.g., Jenkins, GitLab CI).
• Experience serving AI agents via FastAPI, packaging and deploying in OCI-compliant environments and OCP.

Responsibilities

• Architect and implement stateful multi-agent networks and/or workflows.
• Build and optimise end-to-end RAG pipelines with diverse data sources.
• Develop AI microservices and deployment in OCI-compliant environments with OpenShift.
• Implement AI evaluation, tracing, observability, and logging to monitor performance.
• Collaborate with software teams to define data contracts and ensure alignment with business objectives.

Skills

Problem solving

Communication

Self-motivation

Team collaboration

Fast learner

Agile mindset

Tools

Python

LangGraph

LangChain

FastAPI

OpenShift

Jenkins

GitLab CI

OCI

Vector DB

Graph DB

NoSQL

RDBMS

Job description

We are looking for an AI Developer to play a key role in building our agentic reasoning engines. You will own the intelligent layer of our bespoke enterprise system, ensuring our AI agents are reliable, secure, and capable of solving complex workflows. You will be involved in developing sophisticated RAG capabilities and stateful multi-agentic networks, moving beyond simple chat to high-consequence autonomous systems.

What will you do?

1. Agentic AI Architecture & Development Design and implement stateful multi-agent networks and/or workflows using LangGraph and/or LangChain . Architect and implement multi-layered guardrails to ensure agent actions remain within business scope, enterprise safety and policy boundaries. Build guardrails to mitigate risks such as prompt injection and data leakage. Develop sophisticated reasoning engines capable of handling complex, high-consequence autonomous workflows.
2. RAG Pipeline Development & Optimisation Build and optimise end-to-end RAG (Retrieval-Augmented Generation) pipelines , focusing on high-precision retrieval and semantic search. Integrate diverse data sources including vector DB, graph DB, NoSQL, and RDBMS. Implement advanced RAG techniques such as chunking strategies, query expansion, re-ranking, and hybrid search. Apply prompt engineering and context engineering techniques to enhance retrieval quality.
3. AI Microservices & Deployment Build and maintain high-performance AI microservices optimised for OCI-compliant environments . Serve AI agents via FastAPI , packaging and deploying in OCI-compliant environments and OCP (OpenShift Container Platform) . Implement semantic caching and design for parallel LLM invocation to optimise performance. Integrate AI evaluation into CI/CD pipelines (e.g., Jenkins, GitLab CI).
4. Performance Monitoring & Evaluation Use LLM evaluation frameworks to quantitatively measure agent performance. Leverage AI tracing, observability, and logging tools to identify and fix bottlenecks in complex reasoning paths. Monitor and continuously improve agent reliability, accuracy, and efficiency.
5. Cross-Functional Collaboration Partner with software teams to define data contracts and integrate information flow from AI layer to software backend and frontend. Collaborate with stakeholders to ensure AI solutions align with business objectives and technical requirements. Communicate effectively (written and verbal) with technical and non-technical stakeholders at all levels.

An ideal candidate should posses:

Technical Requirements

• Expert-level proficiency in Python , specifically for asynchronous AI applications.
• Mastery of LangGraph and LangChain for building production-grade, stateful systems with human-in-the-loop verification patterns.
• Proven success in developing RAG capabilities , including experience with chunking strategies, advanced techniques like query expansion, re-ranking, hybrid search, etc.
• Deep hands-on experience with vector DB, graph DB, NoSQL, and RDBMS.
• Experience building guardrails to mitigate risks such as prompt injection and data leakage.
• Experience integrating AI evaluation into CI/CD pipelines (e.g., Jenkins, GitLab CI).
• Experience serving AI agents via FastAPI , packaging and deploying in OCI-compliant environments and OCP .
• Proficiency in AI tracing, observability, and logging tools to identify and fix bottlenecks in complex reasoning paths.
• Experience implementing semantic caching and designing for parallel LLM invocation .
• Knowledge of prompt engineering and context engineering techniques.

Professional Experience

• At least 2-5 years of experience in AI/ML development, with substantial work in production-grade agentic AI systems.
• Proven track record of deploying AI solutions in enterprise environments.
• Experience working in Agile or iterative development environments.

Personal Attributes

• Self-motivated with a positive "can-do" attitude.
• Strong problem-solving skills and ability to work independently.
• Excellent communication and collaboration skills.
• Ability to thrive in a fast-paced, innovative environment.

Bonus Qualifications (Preferred)

• Working knowledge of database query languages including SQL and HiveQL (or equivalent).
• Working knowledge of building data ETL pipelines.
• Knowledge in building or fine-tuning deep learning models.
• Knowledge in mathematical statistical analysis.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Familiarity with Design Thinking or Lean UX methodologies.
• Exposure to scaled Agile frameworks (SAFe, LeSS).
• Experience working with distributed teams across multiple time zones.
• Proficiency in using collaboration tools like Mural, Miro, or Jamboard.

Why Join NCS?

Grow with Us

• Work on cutting-edge AI products that shape the future of technology
• Collaborate with talented, passionate teams across research, engineering, and design
• Access continuous learning opportunities and career development pathways

Make an Impact

• Transform AI research into products that solve real problem