Senior AI Engineer

Company Overview

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE—where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value—supporting the office of the CFO to drive end-to-end value creation.

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark.

Data & Analytics

Accordion's Data & Analytics (D&A) team offers cutting-edge, intelligent solutions to a global clientele, leveraging a blend of domain knowledge, sophisticated technology tools, and deep analytics capabilities to tackle complex business challenges.

We partner with Private Equity clients and their Portfolio Companies across diverse sectors, including Retail, CPG, Healthcare, Media & Entertainment, Technology, and Logistics.

This role can be based in any of our US office locations and is a hybrid role with the flexibility to work remotely 2 days a week. Ideal candidates should be local to the desired location.

This position is not eligible for immigration sponsorship.

Accordion Intelligence Lab

The AI Lab is composed of leading software and AI engineers, designing agentic-AI solutions ahead of the market. Our group builds and operationalizes the AI systems that power Accordion’s consulting capabilities, from agentic architectures and RAG pipelines to evaluation frameworks and production observability.

The Lab works closely with practice leaders to translate research findings into scalable, reliable tools that advance Accordion’s AI-driven value creation work for PE clients and their portfolio companies.

About the Role

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.

What You’ll Do

• 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
• Travel to client site as needed

Success in the First 6 Months

• Work with like-mined AI engineers to ship at least one end-to-end agentic solution in a client engagement, from scoping through production
• Establish evaluation infrastructure for your pod’s systems so that quality is measurable and regressions are caught early
• Demonstrate materially faster delivery cycles because of how you use AI tools—not just that you use them
• Build a reputation within the pod and with clients as someone who can be handed a hard problem and trusted to return a working, defensible solution

What You’ll Bring

• 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 Have

• 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

This is a high-velocity environment. Sprints are short, client expectations are high, and we don’t slow down between engagements. If you’ve spent time in large enterprise engineering organizations where velocity is constrained by process, this will feel different.

The people who thrive here are those who find the pace energizing, not exhausting. They ship fast because they’ve internalized good judgment—not because they cut corners. They use AI tools to multiply their output, communicate clearly with non-technical stakeholders, and course-correct quickly when something isn’t working.

The annual salary for this role ranges from: $144,500 to $230,000 USD + significant bonus + benefits. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity.