Junior AI Engineer

Company Description

Ironbook AI is a builder-led company focused on solving complex enterprise AI challenges in data activation, agentic automation, and autonomous data engineering. The company operates a dual model, combining AI-native products—such as an autonomous data migration agent—with a high-impact consulting practice serving leading enterprises across APAC. Teams work directly with modern cloud ecosystems and tools like AWS, Databricks, Confluent, and MinIO to deliver AI systems in real-world production environments. Ironbook AI offers an opportunity to build meaningful systems, experiment with cutting-edge AI, and contribute to the future of enterprise technology. Team members are encouraged to take ownership, collaborate closely, and innovate across the full AI development lifecycle.

Role Description

We are looking for a highly skilled AI Engineer with strong hands-on experience across the AWS AI/ML ecosystem. You will design, build, and deploy AI systems, collaborate with cross-functional teams, and contribute to scalable, production-grade solutions using modern AWS-native tooling. Key Responsibilities:

AI/ML Solution Development

● Build, deploy, and optimize machine learning models on AWS using SageMaker, Bedrock, Lambda, EC2, ECR, and Step Functions.

● Develop end-to-end ML pipelines (training, evaluation, deployment, monitoring).

● Implement vector search, embeddings pipelines, and LLM-based applications using Amazon Bedrock or open-source models.

● Build RAG (Retrieval-Augmented Generation) workflows using AWS services such as OpenSearch / Aurora / DynamoDB.

Data Engineering & MLOps

● Build scalable data pipelines using Glue, EMR, Kinesis, or Lambda.

● Implement MLOps workflows using SageMaker Pipelines, Model Registry, MLflow (if applicable), and CI/CD.

● Monitor and optimize model performance, drift detection, retraining triggers.

Backend & Integration

● Integrate models with applications via REST APIs / async APIs.

● Work with microservices using Python (FastAPI), Node.js, or similar.

● Build inference endpoints optimized for low latency and cost efficiency.

Cloud Architecture & Optimization

● Architect and deploy AI workloads following AWS Well-Architected best practices.

● Optimize compute, storage, and networking for high performance and cost efficiency.

● Implement security, IAM policies, data encryption, and compliance practices.

Required Skills & Experience:

Core AI/ML Skills

● 1-2 years of ML/AI engineering experience, preferably in production environments.

● Strong expertise with:

● AWS SageMaker (training, inference, Pipelines, Model Monitor, Debugger).

● Amazon Bedrock (LLMs, embeddings, fine-tuning or instruction tuning).

● Feature Store, SageMaker JumpStart, Batch Transform.

● Solid experience with deep learning frameworks: PyTorch, TensorFlow, Hugging Face, LangChain (optional but preferred).

● Experience building LLM agents, automation workflows, or RAG-based systems.

Programming

● Strong in Python (mandatory)

● Experience with FastAPI, microservices, containerized ML workloads

● Experience with Git, Docker, CI/CD pipelines

Data Engineering

● Good understanding of data modeling, ETL/ELT concepts

● Experience with Glue, Athena, Kinesis, Redshift, or equivalent

Cloud & DevOps

● Strong hands-on with:

● Lambda

● ECS/EKS (nice to have)

● API Gateway

● CloudWatch

● IAM

● AWS OpenSearch

● Experience integrating third-party telephony systems with Amazon Connect.

Pre-Requisites:

• Singapore Citizens/PRs only to apply