AI Evangelist

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.

What you'll do (Responsibilities)

1. 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.
2. Discover value: Run structured discovery (problem framing, ROI/feasibility) to identify high leverage AI use cases in current applications and greenfield tools.
3. Prototype fast: Build end to end proofs of concept (POCs) using LLMs and agent frameworks, moving from idea prototype in weeks, not months.
4. 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.
5. Agentic systems: Design agent workflows (planning, tool use, retrieval, guardrails) for tasks like intelligent assistance, automation, and decision support.
6. Architecture & ops: Define reference architectures for RAG, tools/plugins, orchestration, observability, evaluation, and cost/performance tuning.
7. Governance: Embed Responsible AI (safety, privacy, security, compliance), data governance, and evaluation frameworks (offline/online) into delivery.
8. Measurement: Establish success metrics (quality, latency, adoption, cost per task, deflection, NPS/CSAT) and run experiments/A B tests to validate impact.
9. Partner ecosystem: Evaluate vendors and open source components; guide build vs buy decisions; contribute reusable assets and playbooks.
10. Champion change: Remove adoption blockers, capture learnings, and scale wins via internal communities, templates, and enablement content.

What you'll bring (Required Qualifications)

1. 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.
2. Demonstrated ability to prototype and code: one or more of Python/TypeScript/Java, plus modern API and microservice patterns.
3. Hands on with LLMs and agentic patterns: prompt engineering, RAG, tool calling/function calling, agents/planners, evaluation.
4. Experience with at least one cloud (Azure OpenAI, AWS Bedrock, Google Vertex AI) and vector/search stacks (Pinecone, FAISS, Elasticsearch/OpenSearch, pgvector).
5. Familiarity with LangChain/LangGraph, LlamaIndex, OpenAI/Claude APIs, and model hosting (managed endpoints or self hosted).
6. Solid understanding of security, privacy, governance, PII handling, prompt injection mitigation, abuse monitoring, and auditability.
7. Interpersonal excellence: persuasive communicator and facilitator; comfortable with exec briefings and hands on pairing with engineers.
8. Strong product sensibilities: able to frame problems, define success metrics, and iterate with user feedback.

Nice to have (Preferred)

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

How we'll measure success (first 6 12 months)

1. 3 5 shipped AI capabilities improving core KPIs (quality, cycle time, cost per task, or revenue uplift).
2. Reusable assets: reference architectures, starter repos, guardrail/eval templates, and adoption playbooks.
3. Organization enablement: >200 employees enabled via workshops/office hours and a sustained internal community of practice.
4. Governance readiness: standardized review and monitoring for responsible AI in production.

Tools & Environment (indicative)

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

Location & Workstyle

1. Location: San Leandro - PT
2. Workstyle: Hybrid collaboration with periodic onsite workshops; occasional travel for stakeholder sessions.