AI Engineer

21 hours ago

Kuala Lumpur, Kuala Lumpur, Malaysia Averis Full time

Role Summary

We are seeking a contract
AI Engineer (Generative AI)
to design, build, and deploy practical GenAI solutions (e.g., document Q&A/RAG, structured extraction, copilots) in collaboration with product and engineering stakeholders. This role is hands-on and delivery-oriented, with a focus on implementing reliable services, evaluation, and clear documentation for maintainability.

Key Responsibilities

  • Design and implement GenAI application workflows, including
    RAG pipelines
    (ingestion, chunking, embeddings, retrieval, prompting).
  • Build
    structured extraction
    solutions (documents → fields/JSON) with validation logic and post-processing where required.
  • Build and maintain
    API services
    (e.g., FastAPI/Flask) to expose GenAI capabilities for integration with internal systems.
  • Create and maintain
    evaluation datasets
    and
    test harnesses
    (accuracy/consistency checks, regression tests, latency tracking).
  • Conduct model and approach comparisons (LLM/embedding/retriever variants) and document trade-offs and recommendations.
  • Apply engineering best practices: version control, reproducible environments, basic testing, logging, and error handling.
  • Produce clear technical documentation: architecture notes, setup guides, runbooks, and handover materials.
  • Collaborate with stakeholders to translate business needs into implementable user stories and deliver incrementally.

Required Qualifications

  • 3+ years experience in a data/ML/software engineering role with strong
    Python
    development skills.
  • Contract Basis - Renewable up to 2 years
  • Practical experience delivering at least one of the following:
  • Retrieval-Augmented Generation (RAG)
    using a vector database,
  • LLM-based information extraction
    into structured outputs,
  • Integration of LLMs via APIs for an end-user workflow.
  • Experience building and consuming
    REST APIs
    and working with JSON schemas / structured outputs.
  • Familiarity with evaluation concepts and metrics; able to implement repeatable testing for model quality.
  • Comfortable working independently, communicating progress clearly, and iterating quickly based on feedback.

Preferred Qualifications

  • Experience with vector stores (e.g., FAISS, Qdrant, Milvus, Pinecone) and retrieval tuning.
  • Familiarity with local LLM tooling (e.g., Ollama) and/or cloud LLM platforms.
  • Experience with document formats and parsing (PDF/XML/HTML), regex-based post-processing, and edge-case handling.
  • Exposure to Docker, CI/CD, and basic observability/monitoring practices.
  • Experience working with security/privacy requirements (PII handling, access controls).
  • Experience with AWS cloud services
    (e.g., deploying services, using managed storage/compute, or integrating with AWS-native tooling).

Technical Skills

  • Python (data handling, APIs, testing), SQL
  • LLM/RAG concepts: embeddings, chunking strategies, retrieval, prompt templates
  • API development (FastAPI/Flask), integration patterns
  • Basic software engineering practices (Git, documentation, reproducibility)
  • Strong problem-solving and debugging ability
  • Clear written and verbal communication with technical and non-technical stakeholders
  • Ability to manage priorities and deliver outcomes in a time-boxed environment

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