We do have openings for AI Evangelist - C2C - San Leandro, CA
Jesinthlobovysystems
Hello,
Hope you are doing well,
Position: AI Evangelist
Type: C2C
Location: San Leandro, CA
Job Description
Role Summary
We’re seeking an inquisitive, hands‑onAI Evangelistwho can turn ideas into shipped capabilities—applying generative and agentic AIto enhance existing products and build new tools. You will partner with product, engineering, data, and business stakeholders todiscover high‑impact use cases, build working prototypes, and guide production adoption, while championing responsible‑AI practices and measurable outcomes.
What you’ll 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) usingLLMs and agent frameworks, moving from idea → prototype in weeks, not months.
· Integrate & ship:Partner with product and platform teams toembed AI features into existing stacks(APIs/services, front‑end surfaces, workflows), hardening POCs for production.
· Agentic systems:Designagent workflows(planning, tool‑use, retrieval, guardrails) for tasks like intelligent assistance, automation, and decision support.
· Architecture & ops:Define reference architectures forRAG, tools/plugins, orchestration, observability, evaluation, and cost/performance tuning.
· Governance:EmbedResponsible AI(safety, privacy, security, compliance), data governance, and evaluation frameworks (offline/online) into delivery.
· Measurement:Establishsuccess 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 you’ll bring (Required Qualifications)
· Total 15+ years of experience in Software engineering, with 8+ yearsin ML engineering (or equivalent) with2+ yearsdeliveringgenerative AIfeatures or platforms end‑to‑end.
· Demonstrated ability toprototype and code: one or more ofPython/TypeScript/Java, plus modern API and microservice patterns.
· Hands‑on withLLMs 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 withLangChain/LangGraph, LlamaIndex, OpenAI/Claude APIs, and model hosting (managed endpoints or self‑hosted).
· Solid understanding ofsecurity, 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), andcost/perftuning.
· Background inMLOps(feature stores, CI/CD for ML, model/version management) orplatform engineeringfor AI services.
· Domain experience in regulated industries (e.g.,financial services, healthcare) andthreat‑modelingfor AI systems.
· Contributions to OSS, internal frameworks, or thought leadership (blogs, talks, playbooks).
How we’ll measure success (first 6–12 months)
· 3–5 shipped AI capabilitiesimproving 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.
Regards,
Jesinth Edin Lobo
