AI Evangelist - San Leandro, CA

Our client seeks an AI Evangelist for a Full Time/12 Months (Contract) project in San Leandro, CA. Below is the detailed requirement

Job Title: AI Evangelist

Work location : San Leandro, CA

Duration: Full Time or 12 Months (Contract)

Role Summary

We're seeking an inquisitive, hands-on AI Evangelist who can turn ideas into shipped capabilities-applying generative and agentic AI to enhance existing products and build new tools. You will partner with product, engineering, data, and business stakeholders to discover high-impact use cases, build working prototypes, and guide production adoption, while championing responsible-AI practices and measurable outcomes

Job Description:

• Bachelor's degree preferably in Computer Science, Information technology, Computer Engineering, or related IT discipline or equivalent experience with 8+ Minimum Experience
• Total 15+ years of experience in Software engineering, with 8+ years in ML engineering (or equivalent) with 2+ years delivering generative AI features or platforms end-to-end.
• Demonstrated ability to prototype and code: one or more of Python/TypeScript/Java, plus modern API and microservice patterns.
• Hands-on with LLMs and agentic patterns: prompt engineering, RAG, tool-calling/function-calling, agents/planners, evaluation.
• Experience with at least one cloud (Azure OpenAI, AWS Bedrock, Google Vertex AI) and vector/search stacks (Pinecone, FAISS, Elasticsearch/OpenSearch, pgvector).
• Familiarity with LangChain/LangGraph, LlamaIndex, OpenAI/Claude APIs, and model hosting (managed endpoints or self-hosted).
• Solid understanding of security, privacy, governance, PII handling, prompt-injection mitigation, abuse monitoring, and auditability.
• Interpersonal excellence: persuasive communicator and facilitator; comfortable with exec briefings and hands-on pairing with engineers.
• Strong product sensibilities: able to frame problems, define success metrics, and iterate with user feedback.

• Discover value: Run structured discovery (problem framing, ROI/feasibility) to identify high-leverage AI use cases in current applications and greenfield tools.
• Prototype fast: Build end-to-end proofs of concept (POCs) using LLMs and agent frameworks, moving from idea prototype in weeks, not months.
• Integrate & ship: Partner with product and platform teams to embed AI features into existing stacks (APIs/services, front-end surfaces, workflows), hardening POCs for production.
• Agentic systems: Design agent workflows (planning, tool-use, retrieval, guardrails) for tasks like intelligent assistance, automation, and decision support.
• Architecture & ops: Define reference architectures for RAG, tools/plugins, orchestration, observability, evaluation, and cost/performance tuning.
• Governance: Embed Responsible AI (safety, privacy, security, compliance), data governance, and evaluation frameworks (offline/online) into delivery.
• Measurement: Establish success metrics (quality, latency, adoption, cost per task, deflection, NPS/CSAT) and run experiments/A-B tests to validate impact.
• Partner ecosystem: Evaluate vendors and open-source components; guide build-vs-buy decisions; contribute reusable assets and playbooks.
• Champion change: Remove adoption blockers, capture learnings, and scale wins via internal communities, templates, and enablement content.