Member of Technical Staff - Applied AI Engineering [Bay Area]

Member of Technical Staff – Applied AI Engineering

Location:

San Francisco Bay Area | Hybrid

AIVista turns foundation models into specialized, governed agents that run mission-critical, regulated enterprise operations reliably  most enterprise AI dies in the last mile. Foundation models are extraordinarily capable in general — yet they don’t know a specific enterprise’s workflows, risk appetite, regulatory interpretations, or the institutional knowledge its teams pass to one another. Without that, projects stall on poor integration, weak governance, and unclear ownership of outcomes.

Closing that gap is the whole job.

As a Member of Technical Staff, you’ll design agentic infrastructure and workflows, customize models to a customer’s domain, and build the software systems that make it all hold up in production — deploying inside some of the world’s largest enterprises, alongside their teams, and owning capabilities end-to-end from prototype to production.

This is a builder’s role at an early‑stage company, for someone who wants to own outcomes rather than tickets. You’ll move with velocity and own the result, hold a relentless bar on reliability where mistakes are costly, and act with conviction while staying flexible as the product and the field evolve.

• Design and deploy agentic systems — build the agentic infrastructure and workflows that power our enterprise AI products.
• Customize models to the domain — adapt foundation models to each customer through tuning, RAG, and related techniques.
• Engineer for production — build scalable, reliable software systems that keep AI products running under real enterprise load.
• Deploy alongside customers — partner with cross‑functional and customer teams to ship capabilities into enterprise environments.
• Iterate relentlessly — improve capabilities, troubleshoot technical issues, and turn customer feedback into product improvements.

• Ship an agent that runs a regulated back‑office process end‑to‑end — pulling from the customer’s systems of record, applying their policies and risk classifications, and executing with a human approval step where the stakes demand it.
• Build the last‑mile specialization layer that teaches a foundation model a customer’s domain — their workflows, client classifications, and regulatory interpretations — and prove it holds up on their real cases.
• Design the agent infrastructure — orchestration, tool integration, memory, and context — that lets agents act reliably across a customer’s real applications and data.
• Stand up the evaluation and guardrail harness that decides whether an agent is reliable enough to run in production against live enterprise systems.
• Instrument governance and observability so every agent decision is auditable, policy‑aligned, and defensible to a regulator.
• Drive down the latency and cost of agentic workflows running at enterprise scale without giving up reliability.

• 8+ years building and shipping AI products, including systems running in production

• Bachelor’s degree in a technical field, or an equivalent combination of education, training, and experience.
• Production‑quality software engineering in Python and other modern languages (Go, Type Script, Rust, or similar), with a track record of taking systems from zero to production — durable software, not one‑off scripts
• A track record of taking AI capabilities from prototype to production — agentic workflows, RAG, or model customization — that stand up to real enterprise use
• Hands‑on experience deploying and operating services on a major cloud platform (AWS, Azure, or GCP), using containers and orchestration (Docker, Kubernetes, Helm, or similar)
• Hands‑on experience building LLM‑powered and agentic applications — model APIs (e.g., OpenAI, Anthropic, Amazon Bedrock), agent frameworks (e.g., Lang Graph, Llama Index), and retrieval‑augmented generation (RAG)
• Bias for action and ownership — you thrive with high autonomy and ambiguity, and ship without sacrificing rigor
• Clear communication skills — comfortable working directly with enterprise customers and across engineering, product, and research