AI Engineer

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.

• 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 Sage Maker, AutoML, and deploy inference endpoints.
• AWS Engineering
  Utilize AWS services (Lambda, API Gateway, S3, Dynamo
  
  DB, Sage Maker, 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.

• Strong proficiency in Python.
• Hands-on experience with LLM inferencing, RAG architectures, and vector embeddings.
• Working knowledge of AWS AI/ML services (Sage Maker, Bedrock, Lambda, etc.).
• Familiarity with classical ML concepts (regression, classification, anomaly detection).
• Experience integrating models into production pipelines.
• Understanding of prompt engineering and evaluation.