AI Engineer

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Role Summary

Build intelligent capabilities using LLM-based inferencing, agentic AI workflows, and RAG-based solutions leveraging AWS-native AI/ML services. Focus on inference orchestration, vector search pipelines, and lightweight model training for predictive maintenance use cases.

Key Responsibilities

LLM & Inference Engineering

Develop AI-driven features using LLMs, agentic patterns, RAG, and vector embeddings.

Orchestrate inference pipelines with Python and AWS AI services.

Build reusable components and prompt orchestration flows.

Predictive Analytics & Light Model Training

Support predictive maintenance using classical ML techniques.

Perform lightweight training with AWS SageMaker, AutoML, and deploy inference endpoints.

AWS Engineering

Utilize AWS services (Lambda, API Gateway, S3, DynamoDB, SageMaker, Bedrock) for scalable AI workflows.

Python Development

Write modular, testable Python code for inference orchestration and backend integrations.

Collaboration & Delivery

Work with product and engineering teams; document AI workflows; participate in design reviews.

Must-Have Skills

Strong proficiency in Python.

Hands-on experience with LLM inferencing, RAG architectures, and vector embeddings.

Working knowledge of AWS AI/ML services (SageMaker, Bedrock, Lambda, etc.).

Familiarity with classical ML concepts (regression, classification, anomaly detection).

Experience integrating models into production pipelines.

Understanding of prompt engineering and evaluation.