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.