AI Software Engineer

Franklin Fitch; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Franklin Fitch is partnering with an AM100 law firm in Atlanta to hire an AI Software Engineer who will design and ship production AI infrastructure. You will build retrieval pipelines, vectorization, and data ingestion, turning firm data into reliable, auditable AI capabilities.

The role is hands-on, spanning backend to user interfaces, with emphasis on scalable, cloud-native systems and governance. Requirements include 4+ years in software engineering with RAG, vector databases, Azure, and NLP

Qualifications

• 4+ years building RAG systems, vector DBs or large data pipelines in production.
• Strong proficiency in C# and Python.
• Experience with cloud-native Azure services and serverless architectures.

Responsibilities

• Architect and maintain data-retrieval and RAG infrastructure, including ingestion pipelines, indexing and vectorization at scale.
• Work across the full stack from cloud-native backend services through to interfaces that expose AI capability to end users.
• Translate business requirements into technical architecture with an auditable, reliable AI system for a professional services environment.

Skills

RAG systems

Vector databases

Data pipelines

C#

Python

Azure

NLP

Embeddings

Job description

AI Software Engineer - AM100 Law Firm - Atlanta

I'm working directly with the Head of Enterprise Architecture and Applications at an AM100 law firm that's investing heavily in AI adoption and innovation across the enterprise. As part of that build-out, they need a hands-on Software Engineer who can design and ship production AI infrastructure, not just consume APIs on top of someone else's platform.

This is a build role. You'd be working on the systems that make the firm's AI initiatives actually function: retrieval pipelines, vectorization, data ingestion, and the plumbing that turns raw firm data into something generative AI tools can reliably use.

What you'll be doing:

• Architecting and maintaining data-retrieval and RAG infrastructure, including ingestion pipelines, indexing, and vectorization at scale.
• Working across the full stack from cloud-native backend services through to the interfaces that expose AI capability to end users.
• Translating business and stakeholder requirements into technical architecture in an environment where the AI roadmap is still being defined, so judgment matters as much as raw execution. Building monitoring and reliability into AI systems so they hold up in a professional services environment where accuracy and auditability matter.

What we're looking for:

• Four-plus years of software engineering experience with direct, hands-on work building RAG systems, vector databases, or large-scale data pipelines in production, not just personal projects. Strong proficiency in C# and Python.
• Cloud-native development experience, ideally Azure, including serverless and managed data services.
• Comfortable working with NLP, embeddings, and LLM-adjacent tooling in a real production context.
• Experience translating ambiguous business requirements into technical scope, since this firm's AI function is still being built out.

If you're currently building AI infrastructure and want to bring that experience into a legal industry environment that's investing seriously in AI right now, I'd like to talk.