This job description for an AI Engineer focuseson the design, development, and deployment of intelligent applicationsusing Amazon Web Services (AWS), Python, and advanced LargeLanguage Model (LLM) techniques like Retrieval-Augmented Generation(RAG).
Core Responsibilities
- LLM Integration & Orchestration: Design and build production-grade applications leveraging LLMs from providers like Amazon Bedrock (e.g., Anthropic Claude, Amazon Nova) or open-source models (Llama, Mistral).
- RAG Pipeline Development: Implement and optimize Retrieval-Augmented Generation (RAG) workflows, including document parsing, semantic chunking, and embedding generation.
- Vector Database Management: Manage and query vector stores such as Amazon OpenSearch Service, Pinecone, or FAISS to enable efficient semantic retrieval.
- Agentic Workflows: Develop autonomous AI agents capable of tool-calling, multi-step reasoning, and complex task orchestration using frameworks like LangChain or LangGraph.
- Deployment & MLOps: Containerize AI services using Docker and deploy them on AWS infrastructure (e.g., Lambda, ECS, SageMaker) with robust CI/CD pipelines.
- Performance Tuning: Optimize model outputs through prompt engineering, fine-tuning (LoRA/PEFT), and managing latency and cost efficiency.
Technical Requirements
- Programming: Proficiency in Python is essential, specifically for developing scalable backend services and AI logic.
- AWS Ecosystem: Hands-on experience with AWS AI services including Bedrock, SageMaker AI, Lambda, S3, and API Gateway.
- AI Frameworks: Deep experience with LangChain, LlamaIndex, and deep learning libraries like PyTorch or TensorFlow.
- API Development: Skill in building and integrating RESTful APIs (e.g., FastAPI, Flask) to connect AI capabilities to enterprise systems.
- Security & Governance: Understanding of Responsible AI practices, including guardrails against hallucinations and ensuring data privacy (GDPR/SOC2).
Preferred Qualifications
- Cloud Certification: AWS Certified Generative AI Developer - Professional or AWS Certified Machine Learning - Specialty.
- Bachelor degree in computer science or related field.
