Principal AI Engineer

Experience Requirements:
Total Experience: 10+ years in Software Engineering.
Python Mastery: 7+ years of professional backend development.
AI/GenAI: 2+ years of hands-on implementation with LLMs (Claude, GPT,
Llama).
AWS: 5+ years architecting cloud-native applications. Key Deliverables
Agentic Workflows: Design and deploy multi-agent systems using LLM orchestration frameworks (e.g., LangGraph, CrewAI) to automate complex cross-functional business processes, targeting measurable efficiency gains.
Production RAG: Build and optimize high-performance Retrieval-Augmented Generation pipelines using Amazon Bedrock and vector databases (e.g., OpenSearch, Pinecone), with clear latency and accuracy targets.
AI Integration: Develop robust FastAPI backends that make model outputs actionable for end-users. Collaborate with frontend engineers on React-based AI interfaces and streaming UI components.
Responsible AI & Guardrails: Implement prompt safety mechanisms, output filtering, bias evaluation, and content moderation to ensure production LLM systems meet security and compliance standards.
Engineering Excellence: Implement automated AI evaluation frameworks (e.g., Ragas, DeepEval), observability tooling (e.g., LangSmith, OpenTelemetry), and CI/CD pipelines for LLM prompts and code. Technical Stack AI: Claude, GPT-series, Hugging Face Transformers, PEFT, and LLM orchestration frameworks (e.g., LangChain, LangGraph).
Agents: Experience with autonomous tool-use, function calling, and state management.
Backend: Python 3.10+, Pydantic, FastAPI, and asynchronous programming.
Cloud: Amazon Bedrock, SageMaker, Lambda (Serverless AI), and RDS/pgvector.
Observability: Experience with AI-specific monitoring, tracing, and evaluation tooling.