Software Engineering Manager

Python Backend Engineering Manager | Hybrid | Atlanta, GA | $180K - $200KA fast-growing AI team is building the foundation for how artificial intelligence is embedded into real-world legal products - from LLM-powered features and intelligent automation to retrieval systems and predictive services. They’re looking for a Python Backend Engineering Manager to lead the engineering effort that turns AI prototypes into reliable, scalable production systems. This is a hands-on leadership role at the intersection of backend engineering, platform architecture, and applied AI - ideal for someone who wants to step directly into the world of AI system design and ownership.What you’ll doLead a Python backend engineering team building the core services that power AI-driven product featuresDesign and operate production-grade APIs and services that integrate LLMs, embeddings, and machine learning modelsOwn the architecture for AI workflows: data ingestion, retrieval pipelines, inference services, and orchestration layersWork closely with AI/ML engineers to take models from experimentation into robust production systemsDefine engineering standards for scalability, observability, reliability, and cost-efficient AI usageBuild systems that support both real-time AI responses and large-scale batch processingPartner with product and data teams to identify high-impact AI use cases and translate them into production systemsHelp shape the company’s transition into an AI-first engineering organisationWhat you need6+ years in backend engineering with strong Python expertise in production environmentsExperience building and scaling distributed systems, APIs, or cloud-native backend servicesStrong understanding of system design, microservices, and data-driven architecturesExposure to AI/ML systems (LLMs, embeddings, search, recommendation systems, or similar) - or strong motivation to move into AI infrastructureExperience with cloud platforms (AWS, Azure, or GCP) and modern backend toolingComfort operating in a hands-on engineering leadership role - setting direction while still buildingPragmatic engineering mindset: focused on production reliability, not just prototypesNice to haveExperience working with LLM-based systems, RAG pipelines, or vector databasesExposure to MLOps practices or ML lifecycle tooling (monitoring, deployment, versioning)Background in SaaS, data-rich platforms, or high-scale consumer applicationsExperience helping a traditional backend team transition into AI-enabled systems