AI Data Engineer -Remote

Job Summary Client is seeking a Senior AI Data Engineer to design and build the agentic systems. This role sits at the intersection of LLMs, financial data, and production infrastructure. The Data in this title refers to Data Science and applied Machine Learning — not warehousing, ETL, or data lake engineering. We are looking for candidates with a strong Data Science foundation (ML/NLP/GenAI) whose most recent experience reflects deep engineering ownership. You will architect and productionize intelligent agent systems capable of reasoning, planning, and executing across complex financial workflows. The ideal candidate has spent the last 1–2 years building multi-agent orchestration systems and fine-tuning LLMs that are fully deployed in production environments. This is a hands-on engineering role requiring strong Python skills, system design maturity, and real-world production AI experience. The job is Remote - USA / Europe / Israel - with a 5-hour overlap with EST hours Role Responsibilities You will: Design and build single and multi-agent systems with structured planning, memory management, and tool use Develop agentic workflows using LangGraph or equivalent orchestration frameworks Build and operate Model Context Protocol (MCP) servers with secure schemas, permission boundaries, and tool governance Integrate LLMs via SDKs, managing structured prompting, tool calling, and output validation Fine-tune LLMs and deploy optimized models into production Define and operate LLM evaluation pipelines covering quality, correctness, regressions, latency, and cost Build production-grade observability systems including logging, tracing, retries, and state management Optimize agent performance and cost efficiency from prototype to scaled production Mentor engineers and establish best practices for applied agentic AI systems Role Requirements 5+ years of software engineering experience 2+ years building production AI/ML systems Recent (last 1–2 years) hands-on experience building multi-agent orchestration systems in production Experience fine-tuning LLMs and deploying them into live systems Strong Python skills and API design expertise Hands-on experience with agentic architectures and tool-calling frameworks Practical experience building and operating MCP servers Experience designing and running structured LLM evaluation pipelines Familiarity with RAG pipelines, embeddings, and vector databases Strong production engineering mindset with a focus on reliability and observability Preferred Qualifications Background in Machine Learning, NLP, or Generative AI Experience with financial data, quantitative systems, or crypto/DeFi (not mandatory) Experience with distributed systems or high-throughput production pipelines Experience at AI-forward companies such as AI21 Labs, Lemonade, Torq, Carbyne, or similar Contributions to open-source AI/ML tooling Ideal Candidate Profile The ideal candidate began their career in Data Science or Machine Learning and has evolved into a production-focused AI Engineer. They are comfortable designing intelligent systems end-to-end — from modeling and evaluation through to deployment, observability, and optimization. This is not a traditional data engineering role focused on warehousing or analytics infrastructure. It is a production AI systems engineering role centered around agentic architectures and applied LLM systems.