AI Data Engineer at MLabs

, , Italy -

Full Time

Start Date

Immediate

Expiry Date

05 Jul, 26

Salary

150000.0

Posted On

06 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Agentic systems, LLM integration, LangGraph, Model Context Protocol, RAG pipelines, Vector databases, Financial data analysis, Distributed systems, API design, Prompt engineering, Observability, Infrastructure management, DeFi, Quantitative analysis

Industry

IT Services and IT Consulting

Description

Senior AI Data Engineer (Agentic Systems) Location: USA / Europe / Israel - with a 5hour overlap with EST hours Compensation: $130K - $150K We are hiring on behalf of our client who build the technology that powers safer, more accessible financial markets. Our risk management systems, oracles, and AI models currently secure over $200 billion in assets across the world's largest decentralized protocols, having processed more than $5 trillion in transaction volume. They recently launched a pioneering Financial Intelligence Platform that transforms complex market data into actionable insights, bringing institutional-grade intelligence to every participant in the ecosystem. The Role: They are looking for a Senior AI Data Engineer to design and build the agentic systems powering their intelligence platform. You will work at the intersection of LLMs, financial data, and production infrastructure, creating intelligent agents that reason, plan, and execute across complex financial workflows. Responsibilities: Agentic Systems: Design and build single and multi-agent systems incorporating planning, memory, and tool use. Infrastructure: Build and operate MCP servers with secure schemas and permissions. Workflows: Develop sophisticated agentic workflows using LangGraph or equivalent frameworks. LLM Integration: Manage prompts, structured outputs, and tool calling via SDKs. Evaluation: Define and run LLM evaluation pipelines for quality, correctness, latency, cost, and regressions. Observability: Build reliability infrastructure, including logging, tracing, retries, and state management. Performance: Optimize performance and cost-efficiency from prototype to production. Mentorship: Establish agentic best practices and mentor junior engineers. Interview Process Recruiter / HR Call - 30min screen with recruiter Hiring Manager Interview - 30min screen with hiring manager to check technical fit Technical Interview - 1 hour technical interview with the Head of Product & AI to check their software engineering skills, syntax, security, programming Technical Interview - 1 hour technical interview with the Chief Data Scientist to check technical understanding for AI, LLMs, RAG etc Founder / CEO Interview - 30min screen with CEO to check motivation for role and company They need an engineer who moves with precision and understands how to bridge the gap between AI research and production-grade financial software. Experience: 5+ years of software engineering, with at least 2+ years specifically building production-level AI/ML systems. Agentic Expertise: Hands-on experience with agentic architectures, tool calling, and LangGraph (or equivalent). Protocol Knowledge: Practical experience with Model Context Protocol (MCP) servers. Evaluation Skills: Demonstrated experience designing and operating LLM evaluation pipelines. Technical Stack: Strong Python proficiency and API design skills. Retrieval Systems: Familiarity with RAG pipelines, vector databases, and embedding-based retrieval. Preferred Qualifications: Prior experience with financial data, DeFi/Crypto, or quantitative analysis. Background in distributed systems or high-throughput data pipelines. Active contributions to open-source AI/ML projects. Competitive Compensation & Equity: They offer a package aligned with growth, performance, and merit. Professional Growth: Be a foundational member of a rapidly expanding, global technology company with significant room for career advancement. High-Stakes Impact: Work on systems that secure hundreds of billions of dollars and define the future of financial risk management. Talent-Dense Team: Collaborate with world-class data scientists and engineers in a high-performance culture. Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search. Commitment to Equality and Accessibility: At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing human-resources@mlabs.city. MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting legal@mlabs.city.

Responsibilities

Design and build intelligent agentic systems that reason, plan, and execute across complex financial workflows. Manage LLM integration, evaluation pipelines, and production infrastructure to ensure reliability and performance.