Machine Learning Engineer

Note: Looking for Ex-JPMC Candidates with OPT, CPT, GC or USC Visas for Fulltime only. Who 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 #J-18808-Ljbffr