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Open nowPosted today

AI Engineer

ES TECH CONSULTANCY PTE. LTD.

Pay
SGD 3,600 – SGD 4,200 a month
Where
North, Singapore
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Your applicationOpen nowAI EngineerES TECH CONSULTANCY PTE. LTD. · North, Singapore

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted today

The posting

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.

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