Job Description:

- Design, implement, and ship AI features such as assistants/agents, retrieval-augmented generation (RAG), recommendations, and intelligent automation.

- Select, integrate, and fine-tune models (LLMs, vision, speech) using frameworks like PyTorch, TensorFlow, and Hugging Face; evaluate via offline metrics and A/B tests.

- Build robust data and inference pipelines: feature extraction, embeddings, vector search, prompt orchestration, guardrails, and human-in-the-loop feedback loops.

- Productionize AI services with strong SLOs: containerize, deploy, and scale across cloud (AWS/Azure/GCP), optimize latency/cost, and implement caching and batching.

- Implement safety, privacy, and compliance controls: PII redaction, abuse detection, content filters, auditability, and policy-aligned prompt/response constraints.

- Establish evaluation and observability: telemetry, experiment design, drift detection, red-teaming, and continuous improvement based on real-world signals.

- Partner cross-functionally to translate product requirements into technical designs; create clear documentation and present trade-offs to stakeholders.

- Stay current with rapidly evolving models, tools, and best practices; prototype quickly and graduate wins into hardened production systems.