AI Engineer

About Job

BNI Sekuritas is building its AI capability to drive smarter, more efficient, and scalable solutions across the firm. We're looking for an AI Engineer (third party contract) to design, build, and deploy production-grade AI and Generative AI solutions, from RAG pipelines and AI agents to cloud-based and on-premise LLM deployments.

This is a hands-on engineering role. You'll work closely with technology and business teams to turn AI use cases into reliable, secure, and scalable solutions. You'll be involved across the full AI engineering lifecycle from model and architecture selection, experimentation, and development to deployment, monitoring, and continuous improvement.

What you'll do

• Design, build, and deploy Generative AI and agentic AI solutions on enterprise cloud AI platforms such as Google Vertex AI, Azure AI Foundry, Alibaba Model Studio, or AWS Bedrock
• Develop production-ready RAG (Retrieval-Augmented Generation) pipelines, including embedding, chunking, retrieval, reranking, and vector database implementation
• Build and orchestrate AI agents and multi-agent workflows using frameworks such as LangChain/LangGraph, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen
• Develop AI services and integrations using Python, APIs, microservices, Docker, and Kubernetes
• Work with LLMs such as Llama, Qwen, Mistral, GPT, Gemini, or equivalent models to develop and optimize AI applications
• Apply prompt engineering and lightweight fine-tuning techniques, including LoRA/QLoRA, where appropriate
• Design and implement model evaluation, monitoring, and performance-improvement processes, including identifying and addressing model drift
• Apply MLOps practices including model/version management, experiment tracking, and CI/CD using tools such as MLflow or Weights & Biases
• Collaborate with IT, data, and business stakeholders to translate business requirements into scalable AI solutions
• Ensure AI solutions comply with relevant IAM, data governance, security, and cost-management requirements
• Evaluate AI architecture, models, frameworks, and cloud services based on performance, scalability, security, and consumption-based cost
• Contribute to the development of internal AI engineering standards, reusable components, and best practices

What we're looking for

• Bachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or a related field
• Strong programming skills in Python
• Hands-on experience with Machine Learning / Deep Learning frameworks, particularly PyTorch and/or Hugging Face Transformers
• Proven experience building and deploying GenAI or agentic AI solutions in at least one enterprise cloud AI platform:
  • Google Vertex AI / Agent Builder / Agentspace
  • Azure AI Foundry
  • Alibaba Model Studio
  • AWS Bedrock
• Hands-on experience developing RAG pipelines and working with vector databases
• Experience with one or more agent orchestration frameworks, such as LangChain/LangGraph, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen
• Good understanding of machine learning fundamentals, including supervised and unsupervised learning, NLP, and recommendation systems
• Good understanding of modern LLM architectures and capabilities
• Familiarity with prompt engineering, model evaluation, monitoring, and fine-tuning techniques
• Experience with API integration, microservices, containerization, and production deployment
• Understanding of MLOps, CI/CD, model versioning, and experiment tracking
• Understanding of cloud IAM, data governance, security, and consumption-based cloud billing
• Strong problem-solving skills and the ability to translate business requirements into practical technical solutions
• Strong communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders

Nice to have

• Experience deploying LLM inference engines in on-premise environments, such as vLLM, TGI, Triton, Ollama, or llama.cpp
• Experience working with GPU infrastructure and LLM serving optimization
• Experience in financial services, banking, or capital markets
• Experience building enterprise AI solutions involving sensitive or regulated data
• Familiarity with AI governance, responsible AI, model risk management, and security considerations for enterprise AI