Directly building with LLMs, RAG, agents and rapid AI prototypes; hands-on vibe coding and production AI tooling.
About the Role
Turn ambiguous business problems into working AI products by working directly with business, product, and engineering teams to define opportunities, prototype solutions, and deliver production systems using LLMs, agents, RAG, and traditional backend services. Own projects end-to-end from discovery through deployment and scale successful solutions across the business.
Job Description
Role
You will act as a Forward Deployed AI Engineer who converts ambiguous business problems into production AI products. The role spans product thinking, AI engineering, and software development, working directly with business, product, and tech teams to identify opportunities, prototype solutions, and deliver production systems.
Key Responsibilities
- Collaborate with business teams to identify high-impact problems addressable with AI.
- Convert ambiguous requirements into clear technical and product solutions.
- Rapidly build and test AI prototypes with real users.
- Develop production applications leveraging LLMs, agents, RAG, APIs, and traditional systems.
- Build backend services, APIs, data pipelines, and system integrations.
- Experiment with models, prompts, and architectures and evaluate performance.
- Create lightweight tools or interfaces when needed for end-to-end delivery.
- Integrate AI systems with enterprise data and workflows.
- Own projects from discovery to prototype, pilot, and production.
- Partner with engineering teams to scale successful solutions and continuously explore new AI capabilities.
Requirements
Core engineering
- Strong engineering fundamentals and high ownership.
- Strong Python skills.
- Experience with backend frameworks such as FastAPI, Flask, or Django.
- Solid understanding of databases, APIs, authentication, distributed systems, and cloud infrastructure.
- Comfortable with SQL and NoSQL databases.
- Ability to debug and work through unfamiliar systems independently.
Applied AI experience
- Hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini).
- Prompt engineering and producing structured outputs.
- Tool/function calling and AI agents or agentic workflows.
- Retrieval-augmented generation (RAG) and vector search.
- Model evaluation and observability; know when to use LLMs and when not to.
Product and interpersonal
- Ability to structure loosely defined problems and prioritize simple, fast solutions.
- Strong product intuition and comfort working directly with stakeholders and users.
- Ability to challenge requirements, identify root problems, and drive solutions to production.
Nice to have
- Experience building AI agents or agentic workflows.
- AWS or similar cloud experience.
- React or frontend experience.
- Workflow/orchestration tools experience.
- Familiarity with internal enterprise tools and regulated industries (e.g., fintech).
- Track record of taking AI prototypes to production.