AI Software Engineer

2+ years of experience building and deploying ML or GenAI systems in production

Strong practical GenAI fundamentals: LLM APIs, context engineering, RAG, tool/function calling, skills, agents and evaluation methodology — you understand why these techniques work, not just how to call them

Data acumen: you can critically assess a dataset, spot distribution problems, and reason about how data quality affects downstream model and business outcomes

Product orientation: you engage with business context naturally, ask the right clarifying questions, and translate ambiguous requirements into well-scoped technical solutions

Solid Python and software engineering fundamentals — clean, testable code, REST API design, debugging, and familiarity with CI/CD

A genuine habit of self-improvement — you follow the field actively, experiment with new models and tools, and bring what's relevant back to the team

Experience with agentic orchestration frameworks

Fine-tuning experience with modern techniques — especially applied to domain adaptation for NLP tasks

PyTorch or Hugging Face familiarity

Familiarity with LLM evaluation frameworks and a structured approach to measuring model quality

Inference optimization awareness — understanding latency/cost/accuracy tradeoffs for LLM solutions

Experience building and deploying robust machine learning APIs in production environments (ideally cloud-based environments such as AWS or GCP)