AI Engineer
Indexed description
Who You Are:
We are seeking a highly practical AI Engineer to design, build, and deploy production-grade AI systems. This is a hands-on engineering role focused on turning real business problems into scalable AI solutions - not academic research. You will work directly with the CTO and product leaders to deliver high-impact AI capabilities across products, operations, and internal platforms. This role is ideal for an engineer who enjoys owning systems end-to-end: from data ingestion and model integration, to APIs, evaluation, and real-world deployment.
What You’ll Do:
- Build LLM-powered systems (RAG, agents, copilots, automation tools)
- Design data pipelines for structured and unstructured data (PDFs, APIs, databases)
- Implement retrieval systems (vector DBs, embeddings, semantic search)
- Integrate and evaluate models (OpenAI, Claude, Gemini, open-source)
- Ship APIs and microservices (FastAPI / Flask / Node)
- Implement prompt engineering, evaluation, and guardrails
- Deploy on cloud (AWS / GCP / Azure / IBM)
- Monitor accuracy, latency, cost, and failure modes
- Work directly with stakeholders to turn fuzzy needs into shipped systems
- Strong Python (and Typescript/Node)
- Experience with LLMs and modern AI stacks
- RAG systems and vector databases (Pinecone, Weaviate, FAISS, Milvus)
- API development (FastAPI / Flask / Express)
- SQL + intermediate data engineering
- Cloud fundamentals (Docker, CI/CD, basic infra)
- IBM Watsonx (all components)
- LangChain / LlamaIndex
- HuggingFace models & pipelines
- Open-source LLMs (LLaMA, Mistral, Mixtral)
- Basic ML evaluation techniques
- Prompt testing frameworks
- Kubernetes (bonus)
- Knowledge graphs / embeddings tuning
- Thinks like a builder, not a researcher
- Can ship MVPs in days, not months
- Understand tradeoffs (accuracy vs cost vs latency)
- Is comfortable with ambiguity
- Can operate without heavy product management
- Enjoys being close to real users and business problems
- Shipped at least 2 production AI features
- Built one full RAG system end-to-end
- Set up evaluation metrics and monitoring
- Become the CTO’s go-to execution partner for AI
- Direct access to leadership
- Real ownership of systems
- Exposure to real business problems
- Freedom to choose tools and architectures
- Ability to shape the company’s AI strategy in practice
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