Machine Learning Engineer

Note:

Looking for Ex-JPMC Candidates with OPT, CPT, GC or USC Visas for Fulltime only.

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.

• 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, Open Search, pgvector, FAISS)
• Build and orchestrate AI agents using frameworks such as Lang Chain, Llama Index, 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

• 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, Open Search, pgvector, FAISS)
• Experience with AI orchestration frameworks:
  Lang Chain, Llama Index, 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