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.

New hires join AI-augmented delivery pods, working alongside AI engineering, data, and product teammates from day one, and initially integrate with the broader Lab team before moving into a specialized pod.
About the Role

This posting covers two levels: Associate Software Engineer and Senior Software Engineer. We're looking for engineers who want to build real agentic systems, not tinker with demos. You'll work under the direction of Staff-level engineers on live client engagements - writing the agent logic, the RAG pipeline, the eval harness - and growing steadily toward owning a workstream yourself.

This is a highly technical, hands-on role in a high-velocity, client-facing environment: sprints are short, client expectations are high, and the work is real from week one. You'll be expected to ship fast, learn quickly, and raise your own bar on what AI-assisted delivery looks like. 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, and are comfortable being early in their AI-native career while still moving at a fast clip. They treat AI tools as something to get genuinely good at using, communicate clearly with non-technical stakeholders, and course-correct quickly when something isn't working.
What You'll Do

1. Build and ship components of multi-agent systems and RAG pipelines for PE-backed portfolio company engagements, under the guidance of more senior engineers
2. Contribute to prompt engineering, tool design, and deployment for production AI systems
3. Write and run evals as part of eval-driven development - define success criteria before you build, not after
4. Help translate client requirements into technical tasks, with support from your pod on the ambiguous parts
5. Build observability into the systems you ship so quality is measurable, not assumed
6. At the Senior level: independently own a workstream (an agentic pipeline, a RAG system, or an eval framework) end-to-end, and present your own architecture decisions to internal and client stakeholders

Success in the First 6 Months

1. Associate: productive on an active client engagement within 30 days; shipping real code and learning the client's data landscape quickly; contributing meaningfully to at least one production system by month 6
2. Senior: shipped at least one end-to-end agentic solution from scoping through production; owns a workstream independently; has built enough credibility with the pod and the client to be trusted with the next hard problem without heavy hand-holding

What You'll Bring

1. Solid Python and/or TypeScript/React fundamentals - you write clean code and can reason about what a framework is doing underneath it
2. Some hands-on exposure to LLM application development: RAG, tool use, prompt engineering, or agent orchestration (production experience preferred; strong coursework/side-project depth considered for Associate level)
3. Genuine use of AI coding tools (Claude Code, Cursor, or similar) in your own workflow - you should be able to talk concretely about where they speed you up
4. Comfort with ambiguity and a fast pace - you won't always get a clean spec, and that's the job
5. Willingness and ability to learn quickly in a client-facing, consulting-style environment

Nice to Have

1. Any finance/PE domain exposure - FP&A, GL data, ERP systems (NetSuite, SAP)
2. Familiarity with LangChain/LangGraph, AutoGen, DSPy, or similar orchestration frameworks
3. Public work you can point to: a GitHub repo, a side project, a blog post - anything that shows how you think about building
4. Any prior internship, co-op, or junior role with client- or user-facing exposure

The annual salary for this role ranges from: $110,000 to $200,000 USD + bonus + benefits, spanning both levels in this posting. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to level, geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity.

THIS POSTING IS FOR A CURRENT VACANCY AND THE EMPLOYER INTENDS TO FILL THIS POSITION BY DECEMBER 9, 2026.