hudsonmanpower · Farmington Hills, Michigan, United States
Skills: Python, SQL, AWS, Docker
Position Summary The AI Software Engineer III will be responsible for designing, developing, and supporting secure, scalable AI-enabled applications. The role focuses on Python development, Generative AI, prompt engineering, Retrieval-Augmented Generation (RAG), document intelligence, AI agents, and enterprise AI integration. Location: Farmington Hills, MI (Onsite – 4 days per week) Experience: 8+ Years Work Arrangement: Onsite – Local candidates strongly preferred Key Responsibilities Design and develop production-quality applications and AI services using Python. Build Generative AI capabilities using Amazon Bedrock and Large Language Models (LLMs). Design, develop, test, and optimize prompts, including prompt templates, structured outputs, and reusable prompt patterns. Develop RAG, intelligent search, embedding, and vector retrieval solutions. Build AI agents and agent-based workflows using frameworks such as Strands Agents. Develop reusable AI services and APIs using FastAPI and Pydantic. Integrate AI solutions with structured and unstructured enterprise data. Support document processing and extraction using Amazon Textract. Implement vector-based retrieval using PostgreSQL/pgvector. Establish safeguards for AI output quality, reliability, validation, traceability, and secure data handling. Optimize AI solutions for performance, latency, token usage, and cost. Develop automated testing and evaluation capabilities for AI solutions. Collaborate with application, data, DevOps, QA, architecture, cybersecurity, and business teams. Required Qualifications 8+ years of experience in Python development, software engineering, AI/ML engineering, or related fields. Strong hands-on experience with Python, REST APIs, FastAPI, Pydantic, and boto3. Experience with Amazon Bedrock, LLM integration, and Generative AI solutions. Strong expertise in prompt engineering, including prompt design, testing, optimization, structured outputs, and evaluation. Experience with RAG, embeddings…
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