Skills : Python, Typescript,
Embeds directly inside a customer's environment (on-site, remote, or in the customer's own cloud) to scope, build, and ship an AI system end-to-end. Owns the outcome — not just the code — acting as a hybrid of software engineer, solutions architect, and customer success manager. Integrates AI (often LLM/RAG-based) into the client's existing data pipelines and workflows, builds domain-specific evaluation frameworks, and iterates with the client until the system is production-grade and trusted.
Job Description:
Embeds directly inside a customer's environment (on-site, remote, or in the customer's own cloud) to scope, build, and ship an AI system end-to-end. Owns the outcome — not just the code — acting as a hybrid of software engineer, solutions architect, and customer success manager. Integrates AI (often LLM/RAG-based) into the client's existing data pipelines and workflows, builds domain-specific evaluation frameworks, and iterates with the client until the system is production-grade and trusted.
Key Skills:
- Production-grade coding: Python (required), plus TypeScript, Rust, or Go for platform work
- LLM application development: prompting, RAG pipelines, agent design, evaluation frameworks (LangChain, LangSmith, Braintrust, Maxim AI)
- SQL and data engineering (Spark, ETL pipelines)
- Cloud/infra fluency: AWS/GCP/Azure, Docker, Kubernetes
- Strong communication — able to translate between technical and business stakeholders in the same meeting
- Rapid problem decomposition and scoping ambiguous requirements
Experience Required:
- 3–8+ years software engineering experience; FDE-specific experience not mandatory
- Background in implementation, technical consulting, solutions engineering, or customer-facing technical roles is a strong plus
- Portfolio of shipped/deployed systems weighs more heavily than academic credentials