Our client is a venture-backed technology company building an enterprise AI context layer used by organizations to make AI assistants genuinely useful inside their own business. Rather than stopping at a chat interface, the platform builds a connected map of the systems, teams, and information a company already runs, so AI can act with full organizational context instead of relying on manually pasted input.
The company also operates an intelligent model routing layer that distributes work across leading closed and open-source models, holding output quality constant while significantly reducing inference cost. The platform serves both enterprise and self-serve customers, with an early customer base of established technology organizations.
Role Overview
Our client is hiring a Senior Software Engineer to help build the foundation of their platform as one of the first engineers on the team. This role carries substantial ownership across the full product lifecycle, including architecture, development, deployment, and iteration. Engineers work across AI systems such as context graphs, model routing, and high-volume inference management, as well as traditional full-stack application code.
You will collaborate directly with engineering and product leadership to translate broad, loosely defined problems into production systems. This role is well-suited to engineers who have built something from zero to one, who prefer setting technical direction over inheriting it, and who use AI aggressively in their own workflow to compound their output. This position is based in Atlanta, Georgia, and is on-site five days per week. The company is looking for candidates already in the Atlanta metro area who are excited about in-office collaboration. The Senior Software Engineer will also mentor junior engineers and help establish engineering practices as the team grows.
Responsibilities
Platform and AI Development
- Design, build, and deploy production features across AI systems, backend services, and application code
- Own features from problem definition through implementation, release, and monitoring
- Build and improve context graph infrastructure that maps relationships across connected enterprise systems
- Develop and tune model routing logic across closed and open-source LLMs
- Implement and manage local model deployment and high-volume inference workloads
- Integrate with external APIs and data sources while handling complex and inconsistent response structures
Architecture and Technical Ownership
- Guide early architecture decisions that will scale as the company and customer base grow
- Make and defend technical trade-offs in areas with limited precedent
- Improve system reliability, scalability, and operational efficiency
- Use AI tooling aggressively to increase velocity and output quality, with a substantial token budget provided
- Investigate and resolve production issues with strong ownership and urgency
Collaboration and Mentorship
- Partner with product on vision, strategy, and roadmap, including surfacing technical constraints early
- Mentor junior engineers and contribute to hiring as the team grows
- Participate in architecture discussions and technical planning
- Help establish engineering standards and team culture at an early stage
Qualifications
- 4 to 8 years of professional experience developing production software systems
- Demonstrated zero-to-one experience owning a product or system from design through deployment
- Proficiency in one or more of GoLang, TypeScript, or Python
- Hands‑on experience with AI/ML APIs and prompt engineering
- Experience with Kubernetes, including unmanaged clusters
- Hands‑on experience with cloud platforms (Google Cloud preferred)
- Active daily use of AI development tools such as Claude Code or Cursor, with a clear point of view on how you get leverage from them
- Bachelor's degree in Computer Science or a related field
- Experience mentoring or supporting junior engineers
- Ability to self‑direct and make progress without detailed specifications
- Strong communication skills and the ability to work directly with non‑engineering stakeholders
- Currently based in the Atlanta metro area. This position is on‑site five days per week and is not remote or hybrid.
- Authorized to work in the United States without visa sponsorship
Preferred
- Experience with local LLM deployment and inference cost optimization
- Experience building knowledge graphs, retrieval systems, or context management infrastructure
- Prior experience as an early engineer at a seed or Series A company
- Experience integrating third‑party APIs at scale
- Personal AI projects or side work demonstrating hands‑on model experimentation
- Interest in moving toward engineering management over time
Why This Opportunity
- Join as one of the first engineers and shape technical architecture that will persist as the company scales
- Work across both applied AI systems and core product engineering rather than a narrow slice
- Substantial autonomy and direct access to founders and product leadership
- Work in a fast‑paced environment where features reach production quickly
- Join a fast‑growing venture‑backed technology company with established enterprise customers
- Competitive compensation and equity participation