AI Data Engineer

Basic/ Essential Qualifications:

• Design, build, and deploy AI/ML solutions on AWS from experimentation to production.

• Develop and productionize LLM-based applications, including prompt design, chaining, and evaluations.

• Leverage AWS AI/ML services to build scalable solutions, including:

  • Amazon SageMaker (training, pipelines, endpoints, monitoring).

  • Amazon Bedrock (foundation models, prompt engineering, guardrails, agents).

• Implement MLOps best practices: model versioning, CI/CD, monitoring, rollback, and reproducibility.

• Build secure and compliant AI systems using IAM, KMS, private networking, VPC endpoints.

• Optimize inference cost, latency, and scalability (autoscaling, batching, async inference).

• Collaborate with stakeholders to identify AI use cases and translate them into technical solutions.

• Ensure responsible AI practices, including explain ability, bias awareness, and governance.

Desirable skillsets/ good to have:

• Strong experience in AI / Machine Learning concepts and applied use cases.

• Solid programming skills in Python for ML and AI workloads.

• Hands-on experience with AWS AI/ML services, especially.

• Amazon SageMaker.

• Amazon Bedrock.

• Experience working with LLMs, embeddings, vector search, and RAG architectures.

• Familiarity with ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, etc.)

• Strong understanding of model lifecycle management and MLOps patterns.

• Knowledge of cloud-native architecture and scalable system design on AWS.

  • Experience implementing security and compliance controls for AI workloads.