- Company Name
- Richard Wheeler Associates
- Job Title
- Senior AI Engineer
- Job Description
-
**Job Title**
Senior AI Engineer (Founding)
**Role Summary**
Lead the design, development, and deployment of AI solutions across the investment lifecycle. Own end‑to‑end delivery of internal AI tools, from rapid prototyping to production-grade systems, and serve as the primary technical advisor for AI strategy.
**Expectations**
- Deliver full‑stack AI products that improve sourcing, research, diligence, and portfolio monitoring.
- Operate autonomously with significant technical freedom, balancing speed and quality.
- Collaborate closely with non‑technical stakeholders to translate business needs into technical solutions.
- Maintain high standards of accuracy, security, and responsible AI use.
**Key Responsibilities**
- Build and maintain internal AI‑powered tools, agents, and workflows.
- Design, prototype, and deploy LLM applications (research copilots, RAG systems, workflow automation, data enrichment).
- Clean, ingest, and analyze external datasets; create lightweight ML pipelines.
- Integrate third‑party APIs, datasets, scrapers, and internal systems (CRM, deal‑flow platforms).
- Rapidly validate MVPs with investment teams and transition high‑impact tools to production.
- Develop simple internal UIs (FastAPI, Streamlit, Next.js) for intuitive adoption.
- Implement evaluation, observability, and governance to ensure reliability, security, and responsible use.
- Mentor and educate team members on AI tools, processes, and best practices.
**Required Skills**
- 5+ years in software engineering, applied AI engineering, or ML engineering.
- Proficient in Python.
- Experience building and deploying LLM applications (OpenAI, Anthropic, Gemini, etc.).
- Deep knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, pgvector).
- Strong API integration skills with third‑party datasets, web APIs, and scrapers.
- Product mindset: enjoy crafting prototypes that evolve into polished tools.
- Excellent communication and collaboration with non‑technical stakeholders.
- Familiarity with finance, enterprise data tooling, or B2B SaaS analytics is a plus.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent professional experience).
- Relevant certifications in AI/ML, data engineering, or cloud platforms are advantageous but not mandatory.