Jecona · 2 days ago
Applied AI Engineer
San Francisco, CA
Full-time
Onsite
Entry, Mid Level
$350K/yr - $350K/yr
2+ years expJecona is partnering with a company that is developing an innovative social platform leveraging AI for matchmaking. They are seeking an Applied AI Engineer to design and implement advanced AI systems that enhance user interactions and optimize matchmaking processes.
Management Consulting
Responsibilities
Ship agentic matchmaking from research to production—own the end-to-end loop (retrieval, reasoning, tool use, safety) and drive measurable accuracy improvements
Build a prompt & model evaluation harness (offline + online) to compare prompts/models/policies, support A/B testing, and enable fast iteration
Optimize AI chat systems for lower latency, higher perceived “human-likeness,” and more consistent outcomes across providers
Design and maintain context engineering pipelines (RAG, memory, summarization, compression, grounding) for conversations and matchmaking
Stand up observability for agents (traces, costs, failures, hallucinations, guardrails) and create dashboards that guide product decisions
Collaborate daily with the cofounders and product to translate user problems into agent behaviors, experiments, and shipped features
Write clear, maintainable code; create small internal tools and SDKs other engineers (and AIs) will use
Qualification
Required
2–4+ years of relevant experience or a standout personal portfolio of agents/LLM apps—show us what you've built (GitHub, demos, write-ups)
Strong programming foundations (data structures, algorithms, testing, profiling)
TypeScript (product code, tools, services) and Python (model ops, evals, data) proficiency
Experience building with multiple LLM providers and tool-calling/function-calling; comfortable swapping models and orchestrating fallbacks
Hands-on with RAG (indexing, chunking, embeddings, reranking) and context engineering for reliability and cost/latency trade-offs
Practical prompt engineering and prompt libraries; can reason about failure modes and systematically improve prompts/policies
Ability to define metrics/KPIs (accuracy, latency, cost, safety), run A/B tests, and loop in human feedback for quality
Comfortable with MongoDB in production; familiarity with vector databases (e.g., pgvector/Redis/Pinecone/Weaviate) is a plus
Builder's mindset: thrives with ambiguity, ships quickly, debugs systematically, and sweats the user experience
Preferred
Extra plusses (the more the better): MCP (Model Context Protocol), agent frameworks (LangGraph/CrewAI/Assistants), LLM observability/evals (e.g., Langfuse/Promptfoo/Ragas/TruLens), retrieval & embeddings know-how, safety/guardrails/red-teaming
Company
Jecona
At Jecona, we work with companies on an exclusive and retained basis within the skilled trade and technical (AI, ML, Software, Data, Tech Sales) space.
2-10 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase