AI Evangelist San Leandro California

AI Evangelist (Hands on Technologist)
Location: San Leandro CA

Fulltime/Contract

Role Summary

Were 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.

What youll do (Responsibilities)

• Educate & influence: Lead demos brown bags and workshops to raise AI fluency across product engineering and business teams; translate complex AI concepts into clear outcome oriented narratives.
• 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.

What youll bring (Required Qualifications)

• 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.

Nice to have (Preferred)

• Experience operationalizing AI features: eval harnesses (LLM as judge/human in the loop) observability (trace logs prompt/versioning) and cost/perf tuning.
• Background in MLOps (feature stores CI/CD for ML model/version management) or platform engineering for AI services.
• Domain experience in regulated industries (e.g. financial services healthcare) and threat modeling for AI systems.
• Contributions to OSS internal frameworks or thought leadership (blogs talks playbooks).

How well measure success (first 6 12 months)

• 3 5 shipped AI capabilities improving core KPIs (quality cycle time cost per task or revenue uplift).
• Reusable assets: reference architectures starter repos guardrail/eval templates and adoption playbooks.
• Organization enablement: >200 employees enabled via workshops/office hours and a sustained internal community of practice.
• Governance readiness: standardized review and monitoring for responsible AI in production.

Tools & Environment (indicative)

• Cloud & Models: Azure OpenAI / Bedrock / Vertex; Open weight models where appropriate.
• Frameworks: LangChain LangGraph LlamaIndex semantic search/vector DBs.
• Data & Services: REST/GraphQL event streams RAG over internal content stores; Redis/Elastic; SQL/NoSQL.
• Ops & Quality: GitHub/GitLab CI/CD IaC telemetry (e.g. OpenTelemetry) eval harnesses canary/A B testing.

Location & Workstyle

• Location: San Leandro - PT
• Workstyle: Hybrid collaboration with periodic onsite workshops; occasional travel for stakeholder session
  
  Regards
  Vignesh PG
  Team Lead