AI Project

Project Desciption

Develab is an IT consulting company operating in Malaysia, Singapore and Indonesia. We continuously seek innovation with a mission to help businesses realize their dreams with quality digital solutions and affordable IT consulting services. Our core services are Software Development, IT Consultancy, Project Management and Cloud Computing. Website: develab.io

Role: AI Engineer

We are seeking a talented and motivated AI Engineer in Malaysia and Indonesia to join our dynamic team. The ideal candidate will have hands-on experience building LLM-powered applications, implementing agentic AI workflows, and integrating AI into production environments.

You will work on projects involving LangChain, LangGraph, LangFlow, LangFuse, vector databases, embeddings, prompt engineering, and multi-agent systems. A strong understanding of both AI model orchestration and application-level deployment is essential. If you are passionate about AI, automation, and delivering production-ready intelligent systems, we would love to meet you. Apply now: lutfi.yulia@develab.io

Responsibilities

• Design, implement, and maintain AI-powered applications using LLMs (e.g., OpenAI, Anthropic, Mistral, LLaMA, Claude, Gemini).
• Develop agentic AI workflows with LangChain and LangGraph, enabling multi-step reasoning, memory, and dynamic tool usage.
• Create interactive AI applications using LangFlow for visual orchestration and LangFuse for observability, debugging, and analytics.
• Integrate vector databases (Pinecone, Weaviate, Milvus, ChromaDB, etc.) for embedding storage, semantic search, and retrieval-augmented generation (RAG).
• Build robust prompt engineering frameworks and prompt optimization strategies to ensure accuracy, reliability, and consistency in AI responses.
• Implement and optimize retrieval pipelines combining embeddings, search algorithms, and metadata filtering.
• Develop multi-agent AI systems with specialized roles, inter-agent communication, and autonomous task planning.
• Utilize Docker and Kubernetes for containerization, orchestration, and scaling AI workloads across environments.
• Collaborate with backend teams to integrate AI services via RESTful APIs or gRPC.
• Monitor, log, and fine-tune AI models in production using observability tools like LangFuse, Weights & Biases, or OpenTelemetry.
• Apply AI safety best practices, guardrails, and policy-based filtering to ensure responsible deployment.
• Conduct performance tuning of AI pipelines for latency, throughput, and cost optimization.
• Stay up-to-date with emerging AI frameworks, research papers, and model releases.

Requirements

• Proven experience in building LLM applications with frameworks like LangChain, LangGraph, and LangFlow.
• Strong knowledge of agentic AI concepts, including planning, reasoning, tool usage, and long-term memory.
• Hands-on experience with LangFuse or similar AI observability platforms.
• Proficiency in Python (FastAPI, Flask, or Django) for backend integration of AI services.
• Experience with vector databases (Pinecone, Weaviate, Milvus, ChromaDB) and embedding models.
• Understanding of RAG pipelines, semantic search, and knowledge base construction.
• Experience with Docker and Kubernetes for deploying scalable AI applications.
• Familiarity with CI/CD pipelines (GitLab, GitHub Actions) for AI deployment.
• Knowledge of prompt engineering techniques and model fine-tuning workflows.
• Strong debugging skills with tools like LangFuse, Postman, and Python debuggers.
• Ability to work in a remote, fast-paced environment, both independently and collaboratively.

Preferred to Have

• Experience with model fine-tuning (LoRA, PEFT, QLoRA) and custom dataset preparation.
• Exposure to multi-modal AI (text, image, audio) applications.
• Knowledge of agentic orchestration with frameworks like CrewAI, AutoGPT, BabyAGI, or OpenAI Assistants API.
• Familiarity with AI cost optimization strategies (token budgeting, hybrid model routing).
• Hands-on experience with cloud AI services (AWS Bedrock, Azure AI, Google Vertex AI).
• Contributions to open-source AI projects.
• Strong communication skills with the ability to articulate complex AI concepts to both technical and non-technical stakeholders.