Software Engineer, LLM & Automation

We're seeking Software Engineers to develop scalable systems that integrate large language models (LLMs) with our everyday operational workflows. This role involves designing and deploying automated "pipelines" for tasks like recruiting, finance, and project management-often powered by GPT, Claude, or similar LLMs. We're flexible on your arrangement: we're hiring contractors , part-time , or full-time teammates.

- Architect and maintain automation pipelines combining internal tools with GPT/Claude and other LLMs

- Integrate data across third-party APIs (e.g., ATS platforms, Slack, Google) into unified, automated workflows

- Leverage structured generation (JSON schemas, function calling, etc.) to ensure robust, correct LLM outputs

- Collaborate with Ops and R&D to identify high-impact automation opportunities

- Write production-grade code, with emphasis on modularity, reliability, and error handling

- Deploy, scale, and optimize your solutions in a secure, cloud-based environment

- Document solutions for both technical and non-technical audiences, ensuring easy updates and maintenance

- Have experience programming in Julia and/or Python (3+ years of production-level coding, or equivalent)

- Understand how to integrate LLMs using structured prompts, scaffolding frameworks (LangChain-like), or advanced text generation approaches

- Can handle concurrency, scaling, and performance optimizations in real-world deployments

- Are comfortable with complex API orchestration: dealing with rate limits, OAuth, error retries, etc.

- Have good collaboration and communication skills, especially when gathering requirements from non-technical staff

- Are flexible and creative problem solvers, comfortable with iterative, experimental development cycles

- Embrace data-security best practices (e.g., handling PII, encryption, secrets management)

- Are excited to learn quickly and adapt to new developments in the LLM/AI ecosystem

- Julia : Type-driven or multiple-dispatch approaches; performance tuning, HPC, or scientific computing workflows

- Python : Building microservices with FastAPI, Flask, or other frameworks; advanced concurrency (async, multiprocessing)

- LLM Tools & Frameworks : Familiarity with LangChain, open source LLM clients, or custom chain-of-thought integrations

- Cloud Infrastructure : Docker/Kubernetes, CI/CD pipelines, AWS/GCP/Azure deployment patterns

- Distributed Systems & Queues : Experience with concurrent processing, task queues (Celery, Sidekiq), or event-driven systems

- Database Interaction : Ability to design schemas and queries in PostgreSQL or similar databases

- Security & Privacy : Understanding of OAuth flows, secrets management, data encryption, and role-based access controls