- Yishun North Region (Singapore) Singapore
Full Time AI Engineer Jobs, Salary up to SGD 4,200 in ES TECH CONSULTANCY PTE. LTD. North Region (Singapore) - Maukerja
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Job Description
Responsibilities
AI Engineer (LLM, GenAI & Agentic Systems)
The ideal candidate is an AI engineer who can build and deploy real-world LLM and GenAI solutions.
Experience delivering AI applications, agentic systems, RAG solutions, and AI-assisted engineering practices will be weighted more heavily than frontend framework expertise
About the Role
We are seeking a talented
AI Software Engineer (LLM, GenAI & Agentic Systems)
to join our growing AI Engineering team. This role focuses on designing, building, and deploying production-grade AI solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and AI-assisted software engineering practices.
You will work closely with solution architects, product teams, data scientists, and clients to transform AI concepts into scalable, secure, and business-ready applications. Beyond writing high-quality software, you will play a key role in shaping how AI technologies are applied responsibly and effectively across enterprise environments.
Key Responsibilities
AI Solution Development
Design, develop, test, and deploy AI-enabled applications using Large Language Models (LLMs), Generative AI technologies, and intelligent automation frameworks.
Integrate foundation models and AI services such as OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini, Vertex AI, or equivalent platforms.
Develop AI-powered solutions including:
Enterprise chatbots and virtual assistants
Retrieval-Augmented Generation (RAG) applications
Intelligent document processing solutions
Knowledge management assistants
AI copilots and task automation agents
Multi-agent and agentic workflow systems
Translate business requirements into scalable AI solution architectures.
Partner with stakeholders to prototype, demonstrate, and productionize AI capabilities.
Agentic Systems & Intelligent Automation
Design and implement agent-based architectures and workflow orchestration solutions.
Build systems that leverage tool/function calling, memory management, context engineering, and task planning.
Develop AI workflows that interact with enterprise applications, APIs, and knowledge repositories.
Evaluate and optimize agent performance for accuracy, reliability, latency, and operational efficiency.
Implement suitable safeguards and human-in-the-loop controls for critical business processes.
Retrieval-Augmented Generation (RAG) & Knowledge Systems
Design and implement RAG architectures using embeddings, semantic search, and retrieval pipelines.
Develop ingestion, indexing, retrieval, and ranking workflows for enterprise knowledge repositories.
Optimize retrieval quality, grounding accuracy, and response relevance.
Work with vector search technologies, retrieval architectures, and semantic indexing approaches.
Evaluate retrieval effectiveness using structured testing methodologies and performance metrics.
AI-Assisted Software Engineering
Utilize AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Gemini Code Assist, Amazon Q Developer, JetBrains AI Assistant, Windsurf, or equivalent tools.
Apply prompt engineering and context management techniques to improve software development productivity.
Leverage AI tools for:
Code generation
Unit test generation
Documentation
Refactoring
Code reviews
Technical analysis
Critically review AI-generated outputs for correctness, maintainability, security, compliance, and performance.
Software Engineering & Integration
Develop applications, services, APIs, and automation components primarily using Python and related technologies.
Design and implement RESTful APIs and integrations with third-party platforms and enterprise systems.
Build reusable AI services, SDKs, and internal accelerators.
Apply software engineering best practices including version control, testing, code reviews, and documentation.
Participate in architecture reviews and technical design discussions.
AI Quality, Evaluation & Responsible AI
Define evaluation frameworks for AI applications and establish measurable quality criteria.
Use AI evaluation methodologies and tools to assess:
Accuracy
Grounding quality
Hallucination rates
Latency
User satisfaction
Implement guardrails, monitoring, and governance controls to support responsible AI adoption.
Ensure compliance with organisational security, privacy, data protection, and intellectual property policies.
Promote ethical and responsible use of AI technologies.
Deployment & Operations
Package and deploy AI applications using containerisation technologies such as Docker.
Collaborate with DevOps teams to deploy AI services into cloud and enterprise environments.
Implement application logging, monitoring, observability, and AI performance telemetry.
Support production deployments and troubleshoot operational issues.
Collaboration & Knowledge Sharing
Work closely with business stakeholders, product managers, architects, and data scientists.
Contribute to solution design workshops, proofs of concept, and client demonstrations.
Share best practices and mentor team members on AI engineering methodologies.
Create reusable assets, templates, reference implementations, and technical documentation.
Required Qualifications
Bachelor's Degree, Master's Degree or equivalent practical experience in Computer Science, Information Technology, Engineering, Data Science, or related disciplines.
3+ years of professional software development, AI engineering, automation engineering, or related experience.
Strong hands-on Python development experience.
Demonstrated experience building and deploying AI-enabled solutions using LLMs or Generative AI technologies.
Experience integrating foundation model APIs such as:
OpenAI
Azure OpenAI
Anthropic Claude
Google Gemini
Amazon Bedrock
Hugging Face
Equivalent platforms
Strong understanding of:
Prompt engineering
Context engineering
LLM fundamentals
Embeddings
Vector search
Retrieval-Augmented Generation (RAG)
Experience designing and implementing AI assistants, copilots, chatbots, agentic workflows, or intelligent automation solutions.
Familiarity with software engineering best practices including Git, code reviews, testing, and Agile delivery methodologies.
Strong problem-solving and analytical thinking skills.
Excellent communication and stakeholder engagement skills.
Preferred Qualifications
Candidates with one or more of the following will have an advantage:
Experience building multi-agent systems or autonomous workflows.
Experience with AI orchestration frameworks such as:
LangChain
LlamaIndex
Semantic Kernel
CrewAI
AutoGen
OpenAI Agents SDK
PydanticAI
Equivalent frameworks
Experience with vector databases or retrieval platforms such as:
Pinecone
Weaviate
FAISS
pgvector
Chroma
Milvus
Experience with AI evaluation frameworks such as Promptfoo, RAGAS, DeepEval, or equivalent.
Experience deploying solutions using Docker and container technologies.
Exposure to AWS, Azure, or GCP AI services.
Experience with frontend technologies such as React, Angular, or modern web frameworks.
Familiarity with CI/CD pipelines and DevOps practices.
The ideal candidate is an AI engineer who can build and deploy real-world LLM and GenAI solutions.
Experience delivering AI applications, agentic systems, RAG solutions, and AI-assisted engineering practices will be weighted more heavily than frontend framework expertise
About the Role
We are seeking a talented
AI Software Engineer (LLM, GenAI & Agentic Systems)
to join our growing AI Engineering team. This role focuses on designing, building, and deploying production-grade AI solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and AI-assisted software engineering practices.
You will work closely with solution architects, product teams, data scientists, and clients to transform AI concepts into scalable, secure, and business-ready applications. Beyond writing high-quality software, you will play a key role in shaping how AI technologies are applied responsibly and effectively across enterprise environments.
Key Responsibilities
AI Solution Development
Design, develop, test, and deploy AI-enabled applications using Large Language Models (LLMs), Generative AI technologies, and intelligent automation frameworks.
Integrate foundation models and AI services such as OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini, Vertex AI, or equivalent platforms.
Develop AI-powered solutions including:
Enterprise chatbots and virtual assistants
Retrieval-Augmented Generation (RAG) applications
Intelligent document processing solutions
Knowledge management assistants
AI copilots and task automation agents
Multi-agent and agentic workflow systems
Translate business requirements into scalable AI solution architectures.
Partner with stakeholders to prototype, demonstrate, and productionize AI capabilities.
Agentic Systems & Intelligent Automation
Design and implement agent-based architectures and workflow orchestration solutions.
Build systems that leverage tool/function calling, memory management, context engineering, and task planning.
Develop AI workflows that interact with enterprise applications, APIs, and knowledge repositories.
Evaluate and optimize agent performance for accuracy, reliability, latency, and operational efficiency.
Implement suitable safeguards and human-in-the-loop controls for critical business processes.
Retrieval-Augmented Generation (RAG) & Knowledge Systems
Design and implement RAG architectures using embeddings, semantic search, and retrieval pipelines.
Develop ingestion, indexing, retrieval, and ranking workflows for enterprise knowledge repositories.
Optimize retrieval quality, grounding accuracy, and response relevance.
Work with vector search technologies, retrieval architectures, and semantic indexing approaches.
Evaluate retrieval effectiveness using structured testing methodologies and performance metrics.
AI-Assisted Software Engineering
Utilize AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Gemini Code Assist, Amazon Q Developer, JetBrains AI Assistant, Windsurf, or equivalent tools.
Apply prompt engineering and context management techniques to improve software development productivity.
Leverage AI tools for:
Code generation
Unit test generation
Documentation
Refactoring
Code reviews
Technical analysis
Critically review AI-generated outputs for correctness, maintainability, security, compliance, and performance.
Software Engineering & Integration
Develop applications, services, APIs, and automation components primarily using Python and related technologies.
Design and implement RESTful APIs and integrations with third-party platforms and enterprise systems.
Build reusable AI services, SDKs, and internal accelerators.
Apply software engineering best practices including version control, testing, code reviews, and documentation.
Participate in architecture reviews and technical design discussions.
AI Quality, Evaluation & Responsible AI
Define evaluation frameworks for AI applications and establish measurable quality criteria.
Use AI evaluation methodologies and tools to assess:
Accuracy
Grounding quality
Hallucination rates
Latency
User satisfaction
Implement guardrails, monitoring, and governance controls to support responsible AI adoption.
Ensure compliance with organisational security, privacy, data protection, and intellectual property policies.
Promote ethical and responsible use of AI technologies.
Deployment & Operations
Package and deploy AI applications using containerisation technologies such as Docker.
Collaborate with DevOps teams to deploy AI services into cloud and enterprise environments.
Implement application logging, monitoring, observability, and AI performance telemetry.
Support production deployments and troubleshoot operational issues.
Collaboration & Knowledge Sharing
Work closely with business stakeholders, product managers, architects, and data scientists.
Contribute to solution design workshops, proofs of concept, and client demonstrations.
Share best practices and mentor team members on AI engineering methodologies.
Create reusable assets, templates, reference implementations, and technical documentation.
Required Qualifications
Bachelor's Degree, Master's Degree or equivalent practical experience in Computer Science, Information Technology, Engineering, Data Science, or related disciplines.
3+ years of professional software development, AI engineering, automation engineering, or related experience.
Strong hands-on Python development experience.
Demonstrated experience building and deploying AI-enabled solutions using LLMs or Generative AI technologies.
Experience integrating foundation model APIs such as:
OpenAI
Azure OpenAI
Anthropic Claude
Google Gemini
Amazon Bedrock
Hugging Face
Equivalent platforms
Strong understanding of:
Prompt engineering
Context engineering
LLM fundamentals
Embeddings
Vector search
Retrieval-Augmented Generation (RAG)
Experience designing and implementing AI assistants, copilots, chatbots, agentic workflows, or intelligent automation solutions.
Familiarity with software engineering best practices including Git, code reviews, testing, and Agile delivery methodologies.
Strong problem-solving and analytical thinking skills.
Excellent communication and stakeholder engagement skills.
Preferred Qualifications
Candidates with one or more of the following will have an advantage:
Experience building multi-agent systems or autonomous workflows.
Experience with AI orchestration frameworks such as:
LangChain
LlamaIndex
Semantic Kernel
CrewAI
AutoGen
OpenAI Agents SDK
PydanticAI
Equivalent frameworks
Experience with vector databases or retrieval platforms such as:
Pinecone
Weaviate
FAISS
pgvector
Chroma
Milvus
Experience with AI evaluation frameworks such as Promptfoo, RAGAS, DeepEval, or equivalent.
Experience deploying solutions using Docker and container technologies.
Exposure to AWS, Azure, or GCP AI services.
Experience with frontend technologies such as React, Angular, or modern web frameworks.
Familiarity with CI/CD pipelines and DevOps practices.
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