Role Description
This is a hands-on engineering role focused on building and maintaining AI and ML services on AWS. You will help turn ideas and prototypes into robust, production-ready APIs and ML flows using Amazon Bedrock, Amazon SageMaker, Python, and FastAPI.
- Build and maintain Python + FastAPI services that integrate ML and LLM capabilities.
- Use AWS Bedrock and Amazon SageMaker to develop, deploy, and monitor ML and LLM models.
- Implement and support ML flows/pipelines under the guidance of senior architects.
- Assist with data preparation for model training, validation, and evaluation.
- Contribute to automated testing, CI/CD pipelines, and observability for ML-powered services.
- Write clear documentation for services, endpoints, and ML pipelines.
- Collaborate with product and data teams to translate requirements into technical solutions.
Qualifications
- 2+ years of experience as a software engineer or ML engineer.
- Strong Python skills and experience with frameworks such as FastAPI, Flask, or similar.
- Hands-on exposure to AWS, ideally including services like Amazon Bedrock and Amazon SageMaker.
- Understanding of LLM basics: prompts, responses, error modes, and common limitations.
- Solid ML fundamentals: train/validation/test splits, basic models, and evaluation metrics.
- Basic understanding of ML workflows/pipelines and interest in growing skills in MLOps.
- Comfort working in a remote, async-friendly engineering environment.
Nice to have:
- Experience building proof-of-concepts (POCs) with GenAI/LLMs.
- Familiarity with Docker, Git, and basic CI/CD pipelines.
