Senior AI Engineer

Accordion • Los Angeles, NY

3d • Hybrid

About The Position

We’re looking for experienced AI Engineers to join new AI-augmented delivery pods: purpose-built teams that combine AI engineering, data science, and product management to transform how PE-backed companies run finance and operations. In this role, you will design and build the AI systems that power client engagements: agentic workflows, RAG pipelines, evaluation frameworks, and production-grade tools that operate in real PE environments. You’ll work directly with clients and cross-functional pod teammates from day one, and you will be expected to ship fast, defend your decisions, and continuously raise the bar on what AI-assisted delivery looks like. This is a highly technical, hands-on role. We move at sprint pace—not quarter pace. If you want to move quickly, own outcomes end-to-end, and build things that matter in finance, this is the role.

Requirements

• AI tools are woven into how you work daily—you are materially faster because of it, and you can demonstrate that concretely
• 3–8+ years of software engineering experience with a notable portion in AI/ML systems, agent development, or applied LLM engineering
• Hands-on experience building production LLM applications: RAG systems, agentic workflows, tool use, multi-agent orchestration
• Proficiency in Python; experience with LangChain, LangGraph, AutoGen, DSPy, or equivalent orchestration frameworks
• Strong grasp of evaluation methodologies: designing evals, catching regressions, assessing LLM output quality
• Solid data engineering fundamentals: ETL design, SQL/NoSQL, vector databases, streaming or batch pipelines

Nice To Haves

• Open-source contributions in the AI/ML space
• Finance or PE domain experience: FP&A, GL data, ERP systems (NetSuite, SAP), or financial workflow automation
• Experience building tools for reuse across multiple clients or in multi-tenant architectures
• Prior experience in consulting, professional services, or client-facing delivery environments

Responsibilities

• Design and build multi-agent systems and RAG pipelines that automate financial workflows for PE-backed portfolio companies
• Own the full stack from prompt engineering and tool design through deployment and monitoring
• Practice eval-driven development: define how success is measured before you build
• Translate ambiguous client problems into engineering requirements and deliver results in compressed timeframes
• Present and defend architectural decisions to technical and non-technical audiences—including CFOs and PE operators
• Build observability and evaluation infrastructure to continuously improve production AI system quality

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed