Artificial Intelligence Engineer

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About the Role

AI Engineer

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

• 5+ 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.

Ironbook AI is a builder-led company focused on solving some of the hardest problems in enterprise AI: data activation, agentic automation, and autonomous data engineering. We operate a dual model — a product business developing AI-native systems like our autonomous data migration agent, alongside a high-impact consulting practice that delivers AI solutions for leading enterprises across APAC. Our teams work hands-on with AWS, Databricks, Confluent, MinIO and modern cloud ecosystems to ship real-world AI at scale. Join us if you want to build meaningful systems, push the boundaries of what AI can do, and help shape the next decade of enterprise technology.