Location: Plano, TX •
Onsite, 5 days/weekWho You Are You are a Senior AI/ML Full Stack Engineer who brings deep hands-on experience building production-grade AI applications with Java or Python. You've spent 7-10+ years mastering full-stack development and have moved confidently into agentic AI, RAG architectures, and LLM orchestration. You're just as comfortable designing a vector store retrieval pipeline as you are hardening a CI/CD deployment on AWS. You thrive in fast-paced, in-office environments where live coding and hands-on problem solving are part of the culture, and you're excited to bring responsible AI practices • guardrails, evaluation frameworks, and content filtering • into everything you build.
What You'll Do - Design and build AI/ML applications end-to-end, from architecture through production deployment
- Implement RAG pipelines, including chunking strategies, embedding models, and vector store integration (Pinecone, OpenSearch, pgvector, FAISS)
- Build and orchestrate AI agents using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or CrewAI
- Develop and deploy solutions using AWS Bedrock, Anthropic Claude models, and model invocation APIs
- Apply advanced prompt engineering techniques • system prompts, few-shot, chain-of-thought, tool use, structured outputs
- Build conversational AI experiences, including chatbots (text) and voicebots (speech-to-text, text-to-speech)
- Design and maintain APIs (REST, GraphQL), microservices, and event-driven architectures
- Own CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code for production systems
- Implement evaluation frameworks, guardrails, content filtering, and responsible AI practices across LLM-powered features
What You Bring - Bachelor's degree required
- 10+ years of software development experience (Java or Python), OR 7+ years if entirely full-stack + Agentic AI development experience
- 2+ years hands-on experience building AI/ML applications in production
- Strong proficiency with RAG architectures • chunking strategies, embedding models, vector stores (Pinecone, OpenSearch, pgvector, FAISS)
- Experience with AI orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, or CrewAI
- Hands-on experience with AWS Bedrock, Anthropic Claude models, and model invocation APIs
- Proven prompt engineering skills • system prompts, few-shot, chain-of-thought, tool use, structured outputs
- Experience building conversational AI: chatbots (text) and voicebots (speech-to-text, text-to-speech)
- Proficiency with AWS services (Lambda, Step Functions, API Gateway, S3, DynamoDB, SQS)
- Experience with CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code
- Strong understanding of API design (REST, GraphQL), microservices architecture, and event-driven systems
- Familiarity with evaluation frameworks for LLM outputs
- Experience with guardrails, content filtering, and responsible AI practices
