This AI team is expanding rapidly, with multiple AI‑driven initiatives underway and more in the pipeline. This role is a backfill for a high‑impact contributor and will focus on building and scaling enterprise AI solutions—not just designing prompts, but owning end‑to‑end development and deployment.
Looking for a hands‑on AI Engineer who combines strong software engineering fundamentals with practical LLM experience. You should be comfortable building applications and thoughtfully designing how AI is implemented within them.
This is a high‑visibility, startup‑style environment where your work directly impacts business operations across the organization.
What You’ll Be Working On
- Internal chatbot and AI assistant development
- AI agent initiatives, including UI‑driven agent workflows
- Document ingestion, search, and retrieval systems (RAG)
- AI vision + OCR/ICR‑based solutions
- Enterprise integrations with internal systems (SharePoint, data platforms, etc.)
Key Responsibilities
AI Application Development (Core Focus)
- Design, build, and deploy AI‑powered applications used across business teams
- Develop full‑stack solutions using C# (Blazor), .NET, Python, and SQL
- Build and integrate microservices that support scalable AI functionality
- Own deployments and integrations within cloud environments (Azure preferred)
LLM & Prompt Engineering
- Design and refine prompts for chatbots, copilots, and AI agents
- Apply structured prompting techniques (few‑shot, chain‑of‑thought, etc.)
- Test outputs for accuracy, reliability, and business alignment
- Implement guardrails, governance rules, and validation strategies
RAG & AI Architecture
- Build and maintain retrieval‑augmented generation (RAG) pipelines
- Work with vector databases, embeddings, and semantic search
- Integrate LLMs with internal data sources (SharePoint, blob storage, etc.)
- Contribute to AI patterns such as agent frameworks and multimodal processing
Business‑Focused Delivery
- Translate business needs into technical AI solutions with measurable impact
- Collaborate directly with stakeholders across operations, compliance, engineering, and more
- Clearly articulate what was built, why it matters, and how it’s used
Must‑Have Skills
- Strong software engineering background (not purely data science)
- Hands‑on experience building applications using:
- C# / .NET (Blazor preferred) and/or Python
- SQL / backend systems
- Experience working with LLMs (OpenAI, Azure OpenAI, etc.)
- Familiarity with:
- RAG architectures
- Chatbots / AI assistants
- API integrations
- Cloud experience (Azure preferred; AWS/GCP acceptable)
- Ability to own features end‑to‑end (design → build → deploy)
Nice to Have
- Experience with Azure services (Functions, Service Bus, Cognitive Search, OpenAI)
- Experience with LangChain, Semantic Kernel, or similar frameworks
- AI agent development
- OCR/ICR or document processing systems
- Exposure to enterprise‑scale AI implementations
- AI certifications or strong personal projects
