Artificial Intelligence Engineer

What You'll Do

Build AI Products End-to-End

• Own AI applications from problem definition through architecture, development, deployment, and production operation.
• Translate ambiguous product and business requirements into working software.
• Build across AI/ML, backend services, APIs, databases, frontend interfaces, data pipelines, authentication, infrastructure, and observability.
• Rapidly prototype, test with real users and data, and turn successful ideas into production-grade systems.
• Make pragmatic engineering decisions based on speed, reliability, simplicity, maintainability, and user value.

AI, LLMs & Agents

• Build production applications using commercial and open-source foundation models.
• Design RAG systems, agentic workflows, tool/function calling, structured outputs, memory, human-in-the-loop workflows, and multi-agent systems where appropriate.
• Work with frameworks such as LangGraph, LangChain, Semantic Kernel, LlamaIndex, or equivalent tools.
• Build retrieval systems using embeddings, vector search, BM25, hybrid retrieval, reranking, metadata filtering, and knowledge graphs.
• Design prompt and context-engineering strategies for complex workflows.
• Evaluate model choices based on accuracy, latency, reliability, security, and cost.
• Build automated evaluations and regression tests for AI systems.
• Fine-tune or adapt models when prompting and retrieval are insufficient.

Backend, Frontend & Data

• Build production backend systems primarily in Python using FastAPI, Flask, Django, or similar frameworks.
• Design APIs, asynchronous workflows, background jobs, queues, caching layers, and event-driven systems.
• Work with PostgreSQL, SQL Server, MongoDB, Redis, Snowflake, and other production data stores.
• Build modern applications using React, Next.js, TypeScript, JavaScript, or equivalent frameworks.
• Create interfaces for copilots, conversational AI, workflow automation, analytics, review queues, and operational applications.
• Implement streaming responses, real-time updates, authentication, permissions, and API integrations.
• Build ingestion and transformation pipelines for structured and unstructured enterprise data.
• Work with documents, databases, APIs, event streams, images, logs, and operational datasets.
• Maintain provenance, permissions, metadata, and traceability across enterprise information.

ML & Computer Vision

• Use classical ML or deep learning when it is better suited to the problem than an LLM.
• Build systems involving classification, forecasting, anomaly detection, ranking, recommendations, optimization, or prediction.
• Build computer-vision applications involving detection, classification, segmentation, OCR, tracking, or image/video analysis.