Location: USA / Europe / Israel - with a 5hour overlap with EST hours

Remote | Full time

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.

Requirements

The client needs 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.

Benefits & Perks

• Competitive Compensation & Equity: The client 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.

Interview Process

1. Recruiter / HR Call - 30min screen with recruiter
2. Hiring Manager Interview - 30min screen with hiring manager to check technical fit
3. Technical Interview - 1 hour technical interview with the Head of Product & AI to check their software engineering skills, syntax, security, programming
4. Technical Interview - 1 hour technical interview with the Chief Data Scientist to check technical understanding for AI, LLMs, RAG etc
5. Founder / CEO Interview - 30min screen with CEO to check motivation for role and company