Website:
ironbook.ai
Job details:
About the Role
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
● 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.
Click on Apply to know more.