Member of Technical Staff Applied AI Engineering
Overview
Design and deploy agentic systems that empower enterprise AI products to operate reliably at scale. Own the end-to-end lifecycle from prototype to production while collaborating with cross‑functional teams to solve complex governance challenges. This builder role offers ownership of outcomes and drives impact in a fast‑moving environment.
What You'll Do11
- 1Design and deploy agentic systems — build the agentic infrastructure and workflows that power our enterprise AI products.
- 2Customize models to the domain — adapt foundation models to each customer through tuning and RAG.
- 3Engineer for production — create scalable reliable software that keeps AI products running under real load.
- 4Deploy alongside customers — partner with cross‑functional and customer teams to ship capabilities into enterprise environments.
- 5Iterate relentlessly — improve capabilities troubleshoot technical issues and turn feedback into product enhancements.
- 6Ship an agent that runs a regulated back‑office process end‑to‑end — integrating systems of record and applying policies with human approval steps.
- 7Build the last‑mile specialization layer that teaches a foundation model a customer’s workflow and regulatory rules.
- 8Design the agent infrastructure — orchestration tooling memory and context to enable reliable agent behavior across applications.
- 9Stand up the evaluation and guardrail harness that determines agent reliability for production.
- 10Instrument governance and observability so every agent decision is auditable and policy aligned.
- 11Drive down latency and cost of agentic workflows while maintaining reliability.
Requirements12
- 1Minimum 8+ years building and shipping AI products including production systems.
- 2Bachelor’s degree in a technical field or equivalent experience.
- 3Production‑quality software engineering in Python and other modern languages such as Go TypeScript or Rust.
- 4Track record moving AI capabilities from prototype to production with agentic workflows RAG or model customization.
- 5Hands‑on experience deploying services on AWS Azure or GCP using containers and orchestration tools like Docker Kubernetes Helm.
- 6Hands‑on experience building LLM‑powered agentic applications including model APIs and frameworks such as LangGraph LlamaIndex and RAG.
- 7Bias for action and ownership — thrive with high autonomy and ambiguity.
- 8Clear communication skills for direct collaboration with enterprise customers and internal teams.
- 9Preferred: design and implementation in an industry‑leading AI product as technical leader.
- 10Experience with model optimizations agent protocols and large‑scale data processing.
- 11Advanced degree MS or PhD in Computer Science AI or related field.
- 12Proven independent work demonstrating AI depth such as shipped products open‑source contributions or research.
Salary Insight
$300 - $400k per year
Location
Required Skills
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